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• Fill out the application form online
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Contact Details

sip@ahduni.edu.in | +91.79.61911125

Research Projects

Project Name: Reconstitution of bacterial actin comet motility in cell extracts
Project Description: Pathogenic bacteria such as Listeria can hijack host machinery to form actin comet structures at one end of the bacteria. The force generated by actin polymerisation in these comets drives the propulsive motility of bacteria, which is key to the cell-to-cell spread of pathogenic bacteria. The goal of this internship project would be to reconstitute actin comet-based motility of pathogenic bacteria in Xenopus egg cell extracts and measure biophysical parameters of actin comet motility. By completing this project, the student would acquire hands-on experience in biochemical methods, high-resolution microscopy, and data analysis.
Work Expected of the Student: Student will perform biochemical reconstitution of actin-driven bacterial comet motility in Xenopus egg extracts and use fluorescence microscopy to track movement of actin comets. This will  be followed by motion analysis to extract parameters such as velocity and directionality of bacterial actin comet motility.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Ashim Rai, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Interplay among gene, environment, protein and behavioural readouts explaining plasticity in visual sensitivity and its functional significance in green chromides, a freshwater fish native to India
Project Description: Visual adaptation is governed by opsins, light-sensitive proteins that exhibit wide variation across different light conditions. In this proposal, Professor Ratna Ghosal aims to investigate plasticity in opsin expression in green chromides (GC, Etroplus suratensis), which occur in diverse habitats, from clear freshwater to turbid, brackish waters. Previous research from Professor Ghosal's group showed that GC are highly social, with significant preferences for conspecifics to form shoals. They also demonstrated that GCs are highly dependent on visual cues, both for shoaling and finding food. They performed transcriptomic sequencing to characterise opsins expressed in GC under white-light conditions. Since behavioural expressions in GC depend on visual cues, they hypothesised that under altered light conditions, GC may need to modulate opsin expression to optimise behavioural performance. With this background, Professor Ghosal aims to study plasticity of opsin expressions in GC housed under laboratory conditions (objective 1) and across natural populations, over both temporal and spatial scales.
Additionally, for the laboratory populations (objective 1), Professor Ghosal plans to assess functional significance (via behavioural assays) of opsin expressions, at fixed intervals over time and across different light environments. The proposal also aims to investigate proximate (genetic) mechanisms (objective 3) underlying opsin expression across populations to determine population-level diversity in opsin genes. Overall, the proposal will draw links among genes, environments, proteins, and behavioural processes, and examine how this integration contributes to the fitness of organisms, a fundamental step towards understanding the mechanisms that maintain biodiversity.
Work Expected of the Student: Laboratory analysis of opsin expressions and behavioural recording and analysis
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 29, 2026
Project End-date: July 3, 2026
Faculty: Ratna Ghosal, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Applying a multi-pronged approach to assess the health of a large ectotherm- Mugger Crocodile- across varying habitats
Project Description: Ectothermic animals depend heavily on the ambient environment to regulate their body temperatures, and to do so, they exhibit various behaviours, such as basking, gaping, and shuttling between land and water. In the proposed project, we aim to characterise and compare the behavioural thermoregulatory patterns of two populations of Mugger Crocodiles, one in a relatively unpolluted lentic system (Charotar) and another in a polluted lotic system (Vadodara) within an urban environment. Apart from behavioural readouts, physiological measures, such as hormone levels, are significant for monitoring individual health. Thus, we will measure glucocorticoid (an indicator of physiological stress) and thyroid (an indicator of nutritional state) hormone metabolites in scat samples of mugger crocodiles via standardised protocols. The physiological measures in association with the behavioural thermoregulatory patterns will assess the overall health of the muggers and will compare the local adaptation patterns between the two contrasting environments.
Work Expected of the Student: Analysis of thermal images and video recordings for estimating basking behaviour
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 29, 2026
Project End-date: July 3, 2026
Faculty: Ratna Ghosal, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Investigating spontaneous ion acceleration in expanding RF plasmas
Project Description: Plasma thrusters are essential for satellite maneuvering, but their efficiency is currently limited to around 30 per cent. At Ahmedabad University, we recently discovered that ion beams, streams of high-speed ions driven by steep electric potential drops, can form spontaneously even without the use of external magnets. This project investigates the fundamental origin of these self-forming ion beams to enhance thruster performance and material processing.
Work Expected of the Student: Students will get hands-on experience with an expanding RF plasma source. They will use LabVIEW-based data-acquisition systems to automate measurements of ion beam energy and plasma potential. By analysing how different particle groups interact to create these beams, they can determine if we can develop lighter, more efficient engines for the next generation of space exploration.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Soumen Ghosh, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Multiwavelength study of star-forming dwarf galaxies
Project Description: The project will include developing an understanding of the theory and observational aspects of star-forming dwarf galaxies in the nearby universe. This study is crucial for understanding the evolution of the first kind of galaxies, which formed during the early phases of the universe. The work will involve aspects of data reduction (UV to Radio regime). The student will collaborate with the Astronomy and Astrophysics group at Ahmedabad University.
Work Expected of the Student: Assist in multiwavelength data analysis of a few star-forming dwarf galaxies and in running galaxy simulations.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Samyaday Choudhury, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Nanomedicine to disrupt the immunosuppressive tumour microenvironment in breast cancer
Project Description: Breast cancer remains the most prevalent malignancy globally, with an estimated 2.3 million new cases diagnosed in 2023 alone. While advancements in early detection and personalised therapies have improved survival rates, the heterogeneity of breast cancer, particularly in Triple-negative breast cancer (TNBC), poses significant clinical challenges. TNBC, characterised by the absence of estrogen receptors (ER), progesterone receptors (PR), and HER2 amplification, is associated with aggressive metastasis, early recurrence, and a median survival of just 10 -13 months for metastatic patients. Current standard-of-care treatments rely on cytotoxic chemotherapy like paclitaxel, doxorubicin. However, chemotherapy resistance develops rapidly in 70 per cent of TNBC patients. The effectiveness of treatment remains limited because of tumor heterogeneity, metastasis, and resistance mechanisms originating from the tumour microenvironment (TME), despite recent developments in targeted and immunotherapeutic approaches. Tumour-associated macrophages (TAMs) function as the main regulators of immunosuppression and metastasis between the cellular elements of the tumour microenvironment. The proposed project addresses a critical unmet need in breast cancer treatment: the lack of therapies that effectively target both tumour cells and their supportive TME. By leveraging advances in nanomedicine and immunometabolism, this study pioneers a multifunctional platform to reprogram TAMs, disrupt oncogenic signalling, and enhance anti-tumour immunity.
Work Expected of the Student: The student is expected to engage actively in both the conceptual and experimental aspects of the mentioned project. The student should have a conceptual understanding of breast cancer biology and should have hands-on experience in basic cell culture techniques.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 18, 2026
Project End-date: July 2, 2026
Faculty: Ashutosh Kumar, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Exotic stellar populations in globular clusters
Project Description: The summer intern will be involved in studies of hot and exotic stellar populations (e.g., White dwarf binaries) in Galactic Globular clusters using Hubble data and stellar evolutionary models. It will involve learning the fundamentals of stellar evolution, statistical fitting algorithms, and analysis of multi-band photometric datasets from telescopes such as AstroSat and Hubble. 
Work Expected of the Student: Assist in multiwavelength colour-magnitude diagram analysis, dealing with stellar evolutionary models and isochrones, and modelling spectral energy distribution of stars.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Samyaday Choudhury, Assistant Professor, School of Arts and Sciences

Project Name: Development of an Arduino-based interface for a three-axis magnetometer 
Project Description: The project focuses on using microcontrollers, such as Arduino, to design and implement a computer interface for a three-axis magnetometer. The magnetic sensor measures the magnetic field along three axes: X, Y, and Z. In this design, the sensor is connected to an Arduino board using common communication protocols such as I²C or SPI. The Arduino programme is written to initialise the sensor, read the magnetic field data, and store the values for each axis. Since raw sensor data may contain errors or noise, simple calibration and filtering methods are used to improve accuracy. The measured data can be displayed on the serial monitor or sent to another device for further analysis. This type of interface helps students learn about embedded systems, digital communication, and data processing. The developed system potentially could be deployed using an airborne platform, such as a drone, for geomagnetic sensing. Overall, this project provides hands-on experience at the intersection of electronics, programming, and computer science.
Work Expected of the Student: The student is expected to learn the basics of Arduino programming and sensor interfacing, assist in developing and testing an Arduino-based interface for a three-axis magnetometer, and perform preliminary data acquisition.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 20, 2026
Project End-date: July 3, 2026
Faculty: Raghwinder Singh, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: CBRS network architecture and protocol solutions
Project Description: This research proposes to study the communication reliability of the new Citizens Broadband Radio Service (CBRS) shared spectrum, a new next-generation technology enabler, and develop novel architecture and protocol solutions for CBRS’s sustainable operations. The cloud-based, centralized Spectrum Access System (School of Arts and Sciences) administrator in the incumbent-dominated CBRS shared spectrum often obfuscates available spectrum information and revokes CBRS communication rights without prior information from the auctioned Primary Access License (PAL) and unlicensed General Authorized Access (GAA) users to protect incumbents’ location details and movements. As a result, the communication reliability of the non-incumbents (i.e., PAL and GAA) is affected by current policy frameworks that focus solely on aggregate interference mitigation using environmental sensing capability networks to protect the incumbents, and no existing solutions address the issue. The proposed research will design novel architecture and protocol solution frameworks from the School of Arts and Sciences administrator’s perspective to address communication reliability issues in CBRS.
Work Expected of the Student: Developing incentive mechanism design frameworks for the incumbent-dominated and School of Arts and Sciences-curated CBRS shared spectrum to maximise policymaker incentives and allocate the shared spectrum optimally among multiple competing service providers through novel spectrum allocation policies.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Abhishek Chakraborty, Assistant Professor, School of Engineering and Applied Science
Link to article or webpage relevant to the topic of the project

Project Name: From individuals to colonies: Modelling collective motion and decision-making in ants and social spiders
Project Description: How do simple organisms without centralised control generate coordinated collective behaviour? In this project, students will explore collective motion and decision-making in ants and social spiders through quantitative analysis and modelling. Using experimental observations and agent-based or phenomenological models, interns will test how local interaction rules, noise, and environmental constraints influence emergent patterns such as clustering, polarisation, and collective transport. The project aims to connect biological observations with theoretical frameworks of self-organisation, providing students with exposure to both empirical data and mathematical approaches to the study of collective systems.
Work Expected of the Student: Assist in designing and conducting laboratory and/or field experiments on ants and social spiders, including setting up arenas, observations, and basic animal handling. Collect behavioural data through direct observation and video recordings of group movement, aggregation, and interaction patterns.
Perform basic video processing and behavioral quantification (e.g., trajectories, neighbor distances, zone-based interactions). Prepare a short written summary of findings and present results at the end of the internship. Analyze data to compute collective metrics such as clustering, alignment, spatial density, and temporal correlations.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Jitesh Jhawar, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Modelling mixed-species flocking: How interaction heterogeneity shapes collective motion
Project Description: Mixed-species flocks are widespread in nature, yet how differences between species influence collective motion remain poorly understood. This project will use simple mathematical and computational models to investigate how heterogeneity in interaction rules, sensory ranges, and response strengths affects the emergence of collective motion in mixed-species groups. Interns will develop and analyse agent-based models in which two or more species differ in alignment, attraction, or noise parameters. They will quantify emergent patterns such as polarisation, clustering, and phase transitions between disordered and ordered states. The project will provide hands-on experience in building simulation models, analysing collective metrics, and connecting theoretical predictions to biological intuition about mixed-species grouping.
Work Expected of the Student: Implement simple agent-based models of collective motion (e.g., alignment–attraction–repulsion frameworks) for single- and mixed-species groups.  Introduce heterogeneity between species by varying interaction strengths, sensory ranges, noise levels, or response delays.  Run systematic simulations across parameter ranges and organise results reproducibly.  Compute and visualise collective metrics such as polarisation, clustering, spatial correlations, and phase transitions.  Compare outcomes between single-species and mixed-species systems and interpret results biologically.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Jitesh Jhawar, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Interstitial tongues: Selfhood and sociolinguistics in liminal geographies
Project Description: This project investigates the unique cultural and psychological landscapes of geographic border regions where multiple languages coexist and intertwine. Moving beyond the concept of simple transition zones, it frames these areas as generative liminal spaces—thresholds where fixed national, linguistic, and cultural identities become fluid. The research focuses on the lived experience within these hybrid contexts, where daily communication often involves code-switching, translanguaging, and the emergence of unique dialectal forms.
Central to the inquiry is the sociolinguistic reality of these regions. The project will analyse how language policies, educational systems, and economic forces shape linguistic hierarchies and practices on the ground. It questions how power dynamics between dominant and minority languages are negotiated in markets, civic institutions, and media.
Building on this, the study delves into the consequent formations of the self. It explores how individuals and communities construct identity in a perpetual state of linguistic and cultural negotiation. Does this liminality produce a sense of fractured belonging or a privileged, multifaceted subjectivity? The concept of hybridity is examined not as a simple blend but as a dynamic, sometimes conflictual, process of self-making. Through ethnographic engagement and narrative analysis, the project aims to illuminate how people use their complex linguistic repertoires to navigate, resist, and redefine the borders imposed upon them, ultimately arguing that these interstitial spaces are crucial sites for understanding the future of belonging in an increasingly mobile and interconnected world.
Work Expected of the Student: The student is expected to know multiple languages like Kannada, Tulu, Kodava, Malayalam, etc. They should be able to produce reports and complete literature reviews within the stipulated time.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Safwan Amir, Assistant Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Waste-to-wealth: Laboratory waste glass–derived porous silicon nanomaterial for hydrogen storage
Project Description: The conversion of waste materials into value-added functional nanomaterials offers a sustainable pathway toward clean energy technologies. In this study, laboratory waste glass is utilised as a silicon source to synthesise porous silicon nanomaterials for hydrogen storage applications. Porous silicon will be prepared via controlled chemical reduction followed by acid leaching, enabling the development of a high-surface-area nanostructure with interconnected porosity. The effects of key synthesis parameters on the structural, textural, and hydrogen-storage properties of porous silicon will be systematically investigated. The optimised material exhibits well-developed micro- and mesoporosity, which plays a crucial role in enhancing hydrogen adsorption performance. Hydrogen storage measurements will reveal that the porous silicon nanomaterial demonstrates significantly higher hydrogen uptake compared to non-porous silicon derived under similar conditions. This work presents an effective waste-to-wealth strategy for recycling laboratory glass waste into advanced porous silicon nanomaterials, highlighting their potential as sustainable and low-cost candidates for hydrogen storage applications.
Work Expected of the Student: Project-related literature survey, experiment plan, synthesis of porous silicon nanomaterial using waste glass, parameter optimisation, and investigation of the hydrogen application on the synthesised material.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Sridhar Dalai, Assistant Professor, School of Engineering and Applied Science
Link to article or webpage relevant to the topic of the project

Project Name: AI/ML-driven prediction and optimisation of advanced flue-gas treatment processes for SOx and NOx removal
Project Description: This two-month summer internship project is designed to train students in applying artificial intelligence (AI) and machine learning (ML) techniques to predict and optimise advanced flue-gas treatment processes for sulfur oxides (SOx) and nitrogen oxides (NOx) removal. The primary objective is to bridge experimental work conducted in-house at our laboratory with computational modelling, enabling students to validate experimental results through simulation and predictive modelling. By converting laboratory data into robust computational frameworks, students will gain a deeper understanding of how process parameters influence pollutant removal efficiency and how AI/ML can accelerate optimisation.
Interns will work with MATLAB and machine-learning optimisation tools to develop predictive models based on experimental datasets, focusing on key operational variables such as sorbent dosage, gas flow rates, and reaction kinetics. The project emphasises validation: students will compare experimental outcomes with simulation results, refine models to improve accuracy, and explore optimisation strategies for enhanced process performance. A minor experimental component will complement the computational work, ensuring students appreciate the interplay between laboratory-scale data and predictive simulations.
By the end of the internship, participants will acquire practical skills in data-driven modelling, process validation, and optimisation techniques relevant to chemical engineering and environmental pollution control. The project will not only strengthen their computational expertise but also instill the ability to translate experimental findings into predictive tools, preparing them for advanced research and industry applications in sustainable air pollution management.
Work Expected of the Student: Students will be expected to actively engage in both the computational and experimental aspects of the project. Their primary tasks will include data handling and validation; collecting and analysing in-house experimental data on flue-gas treatment processes; ensuring data accuracy and consistency for computational modelling; and model optimisation by applying AI/ML algorithms to identify optimal operating conditions, validate simulation outputs against laboratory data, and refine models for improved accuracy. Minor experimental work will involve assisting in small-scale experimental runs to generate validation datasets and to gain an understanding of the practical aspects of flue-gas treatment.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: June 30, 2026
Faculty: Snigdha Khuntia, Assistant Professor, School of Engineering and Applied Science
Link to article or webpage relevant to the topic of the project

Project Name: Design and energy analysis of a chemical process flowsheet: A case study using Aspen Plus and Aspen Energy Analyzer
Project Description: This project presents the design and energy analysis of a chemical process flowsheet using process simulation tools, namely Aspen Plus and Aspen Energy Analyzer. A representative chemical process is modelled in Aspen Plus to develop a steady-state flowsheet, incorporating key unit operations, including reactors, heat exchangers, separators, and compressors. Appropriate thermodynamic property methods are selected, and material and energy balances are established to evaluate process performance. Following flowsheet convergence, Aspen Energy Analyzer is used to assess the process's energy consumption and identify opportunities for heat integration. Pinch analysis is performed to determine the minimum heating and cooling requirements, and potential improvements in the heat exchanger network are explored to enhance overall energy efficiency. The results highlight major energy-intensive units and demonstrate the scope for energy savings through effective heat recovery. This case study illustrates the practical application of process simulation and energy analysis tools in the preliminary design and evaluation of chemical processes. The study's outcomes provide useful insights into process optimization and energy-efficient design practices commonly adopted in the chemical industry.
Work Expected of the Student: Literature survey related to the project, understanding of the simulation tool, design and simulation of a case study using Aspen Plus, and finally, conduct the heat integration using Aspen Energy Analyser 
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Sridhar Dalai, Assistant Professor, School of Engineering and Applied Science
Link to article or webpage relevant to the topic of the project

Project Name: Effects of larval density on wing interference patterns in Drosophilids
Project Description: Wing interference patterns (WIPs) are structural colour patterns produced by light interference in thin insect wing membranes. Because WIPs are highly sensitive to nano-scale variation in wing thickness, they are expected to reflect developmental precision and stress. Larval density is a well-known ecological stressor in Drosophilid flies, affecting nutrition, hormonal regulation, and growth stability. This project will test whether larval crowding alters WIPs in the invasive Drosophilid Zaprionus indianus.
Work Expected of the Student: The student will be responsible for planning, executing, and analysing an experimental study on the effects of larval density on wing interference patterns.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Subhash Rajpurohit, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Explainable AI digital twin for zero-touch service management in 6G V2X networks
Project Description: 6G Vehicle-to-Everything (V2X) networks of the future will facilitate a highly dynamic Internet of Everything (IoE) solution through vehicles, roadside infrastructure, sensors, and edge intelligence. These systems are expected to work at ultra-low latency, be highly reliable, and require minimal human intervention. Yet the development of real zero-touch network and service management (ZSM) remains challenging, as many existing AI-driven solutions are built on black-box architectures that lack transparency, trust, and reasoning capabilities.
This funded summer internship project focuses on designing a neuro-symbolic explainable AI (XAI) digital twin for zero-touch IoE service management in 6G V2X networks. This project will utilise neural network models and symbolic reasoning to promote accurate (yet interpretable) decision-making. The model architectures will have to learn time-varying network and contextual dynamics, including signal strength (RSRP), interference (SINR), link quality (RSRQ), user mobility, and communication–computation capacities. Next, symbolic reasoning layers will be used to analyse the learned patterns to explain the various decisions, deduce cause–and–effect relationships, and correct unreliable actions. The creation of a digital twin of the wireless IoE environment will help continuously monitor network behaviour and link information across the network, computation, and context domains. The system will autonomously deploy a closed-loop control system with this twin for user association, uplink and downlink rate adaptation, and service provisioning. In contrast to current ZSM solutions that target isolated resource management tasks, this work provides an all-encompassing, service-aware view of its context.
The internship would provide hands-on experience with 6G V2X systems, explainable AI, and digital twin technologies to facilitate trustworthy, autonomous network management solutions for the next generation of intelligent transportation systems.
Work Expected of the Student: Students will work full-time in the Machine Intelligence, Computing and xG Network (MICxN) Research Lab for a minimum of eight hours per day. Interns will closely collaborate with existing PhD and UG research scholars and actively participate in the lab’s research ecosystem. The expected work includes: Gaining foundational knowledge in 6G V2X networks, Internet of Everything (IoE), explainable AI (XAI), and digital twin technologies; simulating 6G V2X scenarios integrated with a digital twin, using appropriate network simulators and analysis tools to model dynamic vehicular environments; analysing wireless and contextual parameters such as RSRP, SINR, RSRQ, user mobility, latency, and communication–computation trade-offs; assisting in the design and implementation of AI/ML models, including neural-network-based learning and introductory symbolic reasoning for explainability; supporting the development and validation of a digital twin prototype for monitoring, prediction, and closed-loop decision-making; participating in weekly technical discussions, paper-reading sessions, and progress reviews with research scholars and the faculty mentor; maintaining structured documentation of simulations, code, experimental results, and observations. By the end of the internship, students are expected to submit an 8–10 page two-column research report (IEEE-style) summarising the problem formulation, methodology, simulations, results, and key insights. Interns will also deliver a final technical presentation. Outstanding contributions may lead to conference submissions or extended research collaboration.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 18, 2026
Project End-date: June 12, 2026
Faculty: Dhaval K Patel, Associate Professor, School of Engineering and Applied Science
Link to article or webpage relevant to the topic of the project

Project Name: Scientific characterisation of heritage materials: Analytical approaches and conservation implications
Project Description: This summer internship project aims to characterise heritage materials scientifically, focusing on their composition, microstructure, deterioration mechanisms, and conservation needs. The selected interns will work on archaeological and historical materials, including metals (iron and copper alloys), ceramics, and other materials. Students will also be working on the development of conservation methodology, such as gel-cleaning technology for heritage surfaces. This internship is particularly suitable for students in materials science, chemistry, physics, archaeology, conservation, or related disciplines who are interested in applying scientific methods to cultural heritage research. The experience will provide exposure to interdisciplinary research at the interface of science and history, while developing laboratory skills, analytical reasoning, and scientific writing abilities.
Work Expected of the Student: The project will include literature review, data analysis, preparation of samples, scientific instrumentation, and preparation of short report among many others.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Aditya Prakash Kanth, Assistant Professor, Centre for Heritage Management
Link to article or webpage relevant to the topic of the project

Project Name: Calibration of air quality sensors
Project Description: Air quality is directly linked to public health and life expectancy. It is also closely intertwined with climate change, as both share many common emission sources. Accurate measurement of air quality is challenging due to the diversity of emission sources, their varying intensities, and the dynamic nature of atmospheric conditions in a region. Consequently, point measurements of air quality in vast, densely populated urban agglomerations often fail to reflect the true extent of the problem. Furthermore, identifying air pollution sources and quantifying emissions is possible only through multi-point measurements. Low-cost air quality sensors offer a viable alternative to expensive research-grade instruments for such monitoring; however, they must be characterised and validated against standard reference instruments.
Work Expected of the Student: The student will work on low-cost air quality sensors and data from standard reference instruments. They will characterise and calibrate low-cost air quality sensors using advanced statistical and machine-learning tools.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Aditya Vaishya, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: Designing a mechanical aerodynamic air inlet​​​​​​​
Project Description: Reliable measurements of particulate air quality require an effective sampling inlet. A good sampling inlet is one in which particulate losses are minimal and the flow remains laminar. To achieve this, an aerodynamic inlet must be designed and characterised. An aerodynamic inlet is a mechanical interface that ensures laminar airflow within a tube. As part of this project, a mechanical inlet will be designed, fabricated in the workshop, and tested in the field.​​​​​​​
Work Expected of the Student: The student will work on designing a mechanical inlet using 3D design software. Furthermore, using standard fluid-dynamics equations, basic flow simulations will be performed. Once the inlet parameters are fine-tuned, the inlet will be fabricated in the workshop and tested for performance and validation. 
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Aditya Vaishya, Associate Professor, School of Arts and Sciences
Link to article or webpage relevant to the topic of the project

Project Name: When the boss is an AI: Affective response to disagreement in decision-making​​​​​​​
Project Description: Today, AI agents work alongside human resources as team members, providing support and expertise in decision-making. An AI agent may also serve as a team supervisor (algorithmic management). In such a scenario, how would a human team-member respond to a disagreement in decision-making. This project would investigate the differences in affective and behavioural responses of a human subordinate to a disagreement in decision-making when the supervisor is an AI Agent versus a human agent. The study will use experimental research method.​​​​​​​
Work Expected of the Student: The student is expected to assist in literature review; pre-test and pilot test; and preliminary data analysisminary analysis
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 03, 2026
Faculty: Vedant Dev, Assistant Professor, Amrut Mody School of Management
Link to article or webpage relevant to the topic of the project

Project Name: Goal-oriented behaviour in sustainable HRM: A vignette-based study of ethics, creativity, and innovation​​​​​​​
Project Description: This research proposal investigates how Sustainable Human Resource Management (SHRM) can foster ethical and creative innovation by applying the Model of Goal-Oriented Behaviour (MGB) within a vignette-based experimental design. SHRM emphasises balancing economic, social, and environmental goals in HR practices, integrating employee wellbeing, ethical responsibility, and long-term organisational resilience. Creativity and innovation are essential for addressing sustainability challenges, but they must be guided by ethical and sustainable principles. The MGB provides a robust framework for understanding how attitudes, subjective norms, anticipated emotions, and past behaviour shape desires and intentions, ultimately influencing behaviour. By embedding SHRM practices into this model, the study seeks to uncover how organisational cues and HR policies can drive employees toward sustainable innovation.
The research will examine how sustainability-oriented HR practices influence employees’ creative intentions, test the mediating role of anticipated emotions such as pride, guilt, and responsibility in shaping ethical innovation behaviour, and explore how past sustainable behaviour moderates the link between attitudes and intentions. Vignette-based experiments will be designed to manipulate HR policy framing (sustainability-oriented versus profit-oriented), leadership styles (supportive versus authoritarian), emotional cues (anticipated pride versus guilt), and references to past behaviour (success versus failure in sustainability projects). Responses will be measured through Likert-scale items capturing attitudes, norms, emotions, desires, intentions, and creativity ratings, and analysed using structural equation modelling to test the pathways proposed by MGB.
Work Expected of the Student: Data collection
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 12, 2026
Project End-date: June 30, 2026
Faculty: Ekta Sharma, Associate Professor, Amrut Mody School of Management
Link to article or webpage relevant to the topic of the project

Project Name: ESG disclosure and financial performance in emerging markets: The moderating role of institutional voids and local credibility mechanisms​​​​​​​
Project Description: This project examines the conditional relationship between Environmental, Social, and Governance (ESG) performance and Corporate Financial Performance (CFP) in emerging markets, where institutional voids often weaken the credibility of ESG disclosures. While evidence from developed economies generally suggests that higher ESG scores reduce the cost of capital by signaling lower risk, findings from emerging markets remain inconsistent, with ESG sometimes functioning as a costly expenditure rather than a credible investment. This inconsistency arises because weak legal enforcement, regulatory ambiguity, corruption, and limited media scrutiny reduce the reliability of ESG signals, leaving investors skeptical of firms’ sustainability claims. To address this gap, the study integrates firm-level ESG data with publicly available global datasets to capture both institutional voids and informal monitoring mechanisms. Bloomberg ESG scores provide the backbone for firm-level disclosure, while World Bank Governance Indicators, the Sovereign ESG Data Portal, and Transparency International’s Corruption Perceptions Index measure institutional quality. UNCTAD and OECD data contextualise investment climates, while Factiva and LexisNexis capture media visibility as proxies for informal monitoring. Industry-level ESG intensity is derived from Bloomberg sector aggregates and MSCI averages, while India-specific databases such as PROWESS, ACE, TRACXN, and PRIME Info add depth on governance and ownership structures. The conceptual framework posits that ESG disclosure reduces the cost of capital only when validated by strong institutions, peer monitoring, or media scrutiny. By moving beyond a direct-effect model toward a conditional, multi-level framework, this study contributes theoretically to ESG–CFP research, empirically by testing moderators across diverse emerging markets, and practically by offering investors and policymakers insights into when ESG disclosure is most effective.​​​​​​​
Work Expected of the Student: Data mining and compilation
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 12, 2026
Project End-date: June 30, 2026
Faculty: Ekta Sharma, Associate Professor, Amrut Mody School of Management
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Project Name: Decoding the psychology of team success
Project Description: Why do some teams inspire trust, energy, and excellence, while others quietly struggle with disengagement and unequal contribution? What invisible psychological forces shape collaboration? The project examines how individuals perceive one another along two powerful social dimensions—warmth (eg, trust, respect) and competence (capability, effectiveness, reliability)—and how these perceptions shape motivation, contribution, and collective performance over time.
Using real data from project teams, the study tracks how perceptions evolve from the beginning to the end of a project. It also investigates how individual traits, such as core self-evaluation, interact with group-level processes. The goal is not merely to analyse data but to uncover patterns that explain why collaboration thrives or falters.
In this project, you will engage deeply with research design, psychometric validation, multilevel thinking, and data analysis in R. You will learn how to translate complex statistical findings into meaningful insights about teamwork, leadership, and human behaviour. More importantly, you will develop the ability to think critically about groups, not just as collections of individuals, but as dynamic psychological systems.
This type of work is ideal for curious, analytically inclined students who want to move beyond textbook knowledge and contribute to real behavioural research. By the end, students will strengthen their research and analytical skills and gain a richer understanding of what makes teams truly work.
Work Expected of the Student: Data analysis support: Assist in data cleaning, coding, and statistical analysis using SPSS and R, ensuring accuracy and methodological rigour;  Conceptual thinking: Engage deeply with research frameworks, critically examine theoretical models, and explore relationships among variables; Intellectual Engagement: Demonstrate curiosity, attention to detail, and willingness to learn advanced analytical and methodological approaches.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: June 11, 2026
Project End-date: July 3, 2026
Faculty: Samvet Kuril, Assistant Professor, Amrut Mody School of Management
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Project Name: Sustainable fashion and consumer practices: An exploratory study
Project Description: Understand the consumer shift from fast fashion to a circular economy and how to bring an attitudinal change in the consumer purchase behaviour in the fashion industry.
Work Expected of the Student: Data collection, Analysis, and writeup
Expected Qualification of the Student: Pursuing Masters
Project Start-date: June 1, 2026
Project End-date: July 3, 2026
Faculty: Zalak Shah, Assistant Professor, Amrut Mody School of Management
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Project Name: From admission to placement: A data mining study
Project Description: Collection and wrangling (including anonymisation) of student data from university constituents to determine how student characteristics and academic + co-curricular activities influence the career outcomes (including higher studies) of students of Ahmedabad University. Qualitative interviews will supplement appropriate statistical / machine learning analyses to develop actionable insights.
Work Expected of the Student: Data collection, data wrangling, statistical MLanalysis, qualitative interviewing, writing.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: June 22, 2026
Faculty: Amit Das, Senior Associate Dean, Graduate Programmes, Amrut Mody School of Management

Project Name: AI meets optimisation: Building data-driven decision tools
Project Description: Many important decisions in areas such as energy use, logistics, and transportation depend on information that is uncertain or changing. For example, how much demand will occur tomorrow? What will prices look like? How long will the travel take? This project studies how ideas from Artificial Intelligence (AI) can be combined with optimisation methods to make better decisions in such situations.
Students will learn to build simple prediction models from data and then use those predictions inside mathematical optimisation models. The goal is to create a complete decision pipeline: data → prediction → optimisation → insight. The student will gain hands-on experience in structuring problems, working with datasets, and implementing models using accessible Python or Excel-based tools.
No prior research experience is required. The emphasis will be on understanding the intuition behind the methods and translating real-life questions into analytical models. Depending on the student’s interest, the application context may involve smart energy planning, supply chain decisions, or routing and mobility problems.
Work Expected of the Student: Understand the decision problem, prepare relevant datasets, develop basic prediction models for uncertain inputs, and formulate and implement optimisation models using these predictions. By the end of the internship, the student is expected to produce a working prototype and a short report explaining the approach, results, and managerial insights.
Expected Qualification of the Student: Pursuing Bachelors
Project Start-date: May 11, 2026
Project End-date: July 1, 2026
Faculty: Md Shahrukh Anjum, Assistant Professor, Amrut Mody School of Management
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Project Name: Clean water technology
Project Description: Modern water and wastewater treatment technologies, such as electrochemical AOPs, will be employed to abate persistent contaminants sustainably. These technologies leverage the in situ generation of powerful oxidising species to achieve high mineralisation rates without intensive chemical dosing.
Work Expected of the Student: Laboratory experiments, quantification using instruments, analysis and interpretation, writing manuscript.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 11, 2026
Project End-date: July 3, 2026
Faculty: Ramya Srinivasan, Assistant Professor, School of Engineering and Applied Science
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Project Name: Multi-sectoral approach to renewable energy adoption in light of India’s Viksit Bharat 2047 and Net-Zero 2070 targets
Project Description: India's Nationally Determined Contributions (NDCs) targets, first laid out in the Paris Agreement in 2015, proposed to significantly limit the emission intensity of GDP by 2035 compared to 2005 levels. These targets have been subsequently revised but lack sector-specific projections. Considering heavy-polluting sectors such as manufacturing, construction, power generation, transportation, fuel exploitation, and agriculture, we focus on how patterns of final energy consumption and supply to these sectors influence aggregate emission-intensity targets. We also consider the energy intensity of GDP targets for the economy as a whole and how sectoral energy use helps achieve the country's 2047 and 2070 ambitions.
India proposes to become a developed nation, ""Viksit Bharat,"" by 2047 and to achieve net-zero emissions by 2070. Net-zero emissions scenarios, as projected by India Energy Security Scenarios (IESS) for 2047-2070 at five-year intervals, need further investigation. We propose simulating energy and electricity demand and supply paths based on our analysis of the energy and emissions intensity of GDP. On the other hand, India's Viksit Bharat ambitions by 2047 propose that aggregate GDP rise to USD 40 trillion, with significant increases in per capita income to USD 15000-USD 18000. Decomposing sectoral emissions into overall economic growth, sectors' energy efficiency and adoption of cleaner fuels, and the changing economic shares of sectors, we investigate whether energy efficiency plays aa vital role in reducing emissions in the most polluting sectors.
Work Expected of the Student: Expectations as in case of Research Assistant (RA). Familiarity with various data sources in an Indian context and extracting data from .pdf sources. Understanding projected time paths from policy papers/reports. Handling time series data, data analysis, compilation of results and preparing manuscript.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 15, 2026
Project End-date: June 30, 2026
Faculty: Supratim Das Gupta, Assistant Professor, Amrut Mody School of Management

Project Name: Simulation and analysis of quantum phase estimation algorithms
Project Description: Quantum Phase Estimation (QPE) is a central primitive underlying many quantum algorithms, including factoring, search, and quantum simulation. This project aims to understand QPE from a computational and mathematical perspective through simulation and experimentation.
The project has two objectives:

• Learn core quantum algorithms and build educational software:
  The student will implement small quantum circuit simulations in Python and develop clear, well-structured code/notebooks that demonstrate how quantum algorithms extract information from amplitudes and interference.
• Compare different phase estimation approaches:
  The student will implement multiple variants of QPE (standard, iterative, and resource-efficient versions) and perform a comparative study of their accuracy, measurement cost, and robustness using numerical experiments.

Project Name: Impact of Holi Festival Emissions on Aerosol Size Distribution and Black Carbon
Project Description: The festival of Holi is associated with intense short-term emissions due to biomass burning, bonfires (Holika Dahan), firecrackers, and large-scale human activities. These episodic events can significantly alter urban air quality, particularly influencing ultrafine particle number concentrations and black carbon (BC) levels.
This project aims to investigate the impact of Holi-related emissions on aerosol size distribution and black carbon concentrations using high time-resolution data from a Scanning Mobility Particle Sizer (SMPS) and a Black Carbon monitor. The student will analyze particle number size distributions (10–500 nm range) and BC concentrations before, during, and after the Holi period to understand changes in nucleation, accumulation mode particles, and combustion signatures.
The study will help identify whether festival-related emissions lead to new particle formation events, enhanced accumulation mode particles, or spikes in BC concentrations. The analysis will also explore diurnal patterns, particle growth behavior, and correlations between BC and particle number concentrations.
The outcomes of this work will contribute to understanding short-term pollution episodes in Indian urban environments and their implications for exposure.
Work Expected of the Student: The student will work for processing and analyzing high time-resolution Scanning Mobility Particle Sizer and black carbon datasets collected during the Holi period. This includes data cleaning, quality control, time-series visualization, particle size distribution analysis, and correlation analysis between particle number concentrations and BC levels. The student will perform statistical comparisons of pre-, during-, and post-Holi periods to quantify enhancements and identify pollution episodes. Preparation of publication-quality figures and a concise technical report summarizing findings will be expected at the end of the internship. Prior experience in Python is essential, particularly for time-series analysis, data handling, and visualization (e.g., matplotlib or similar libraries). Basic understanding of atmospheric science or air pollution concepts will be considered an advantage.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 4, 2026
Project End-date: July 3, 2026
Faculty: Anil Patel, Assistant Professor, Bagchi School of Public Health
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Project Name: Assessment of Oxidative Potential of Atmospheric Aerosols Across Diverse Regions
Project Description: Atmospheric particulate matter (PM) poses serious health risks not only due to its mass concentration but also because of its ability to generate reactive oxygen species (ROS) in biological systems. This intrinsic property, known as aerosol oxidative potential (OP), is increasingly recognized as a more health-relevant metric than PM mass alone. However, spatial variability in oxidative potential across different regions remains poorly characterized in India.
This project aims to quantify and compare the oxidative potential of PM samples collected from multiple regions representing diverse emission sources and atmospheric conditions. The study will involve chemical extraction of filter samples followed by OP analysis using acellular assays such as the DTT (dithiothreitol). The student will help evaluate spatial differences in OP and investigate associations with chemical composition (e.g., transition metals, organics, secondary species).
The project will contribute to building a regional oxidative potential dataset and improve understanding of how source profiles and atmospheric processing influence aerosol toxicity.
Work Expected of the Student: The student will assist in filter extraction procedures and conduct oxidative potential assays under supervision. Work includes preparation of reagents and standards, careful pipetting, maintaining laboratory logs, and ensuring quality control during experiments. The student will also participate in data processing, calculation of OP metrics (mass-normalized and volume-normalized), and basic statistical comparisons across regions. If chemical composition data are available, the student may perform correlation analyses to explore drivers of oxidative potential. Preparation of figures, tables, and a short technical summary report will be required at the end of the internship. Prior coursework in chemistry, environmental science, or related laboratory experience will be preferred.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 4, 2026
Project End-date: July 3, 2026
Faculty: Anil Patel, Assistant Professor, Bagchi School of Public Health
Link to article or webpage relevant to the topic of the project

Project Name: Modulation of peanut allergens with reference to the oil quality
Project Description: Peanut is one of the most important oil seed crop cultivated and consumed globally. While Western countries have high peanut allergenicity, the Asians do not feel allergic response, but they tend to have a higher sensitization rate. The current project aims to investigate how different abiotic stress affects the peanut allergen response in relation to its oil composition during the developmental phase. This will provide a crucial link to understand a correlation of peanut allergen and oil quality.
Work Expected of the Student: Students will be required to carry out oil extractions from different stressed plant tissues and controls- run GC-FID and analyse the data. They will be also required to work in the greenhouse (field most likely too) alongwith performing some of the molecular techniques such RT-PCR, PCR, gene cloning etc to study the peanut allergens.
Expected Qualification of the Student: Pursuing Masters
Project Start-date: May 18, 2026
Project End-date: June 30, 2026
Faculty: Bhuvan Pathak, Assistant Professor, School of Arts and Sciences