DAU announces Summer Research Internship for non DAU students during May to July 2025.
Student Eligibility
- Currently enrolled UG, PG and PhD students in good academic standing at accredited national institutions are eligible to apply (students currently enrolled at DAU are not eligible for this program).
- Specific eligibility criteria, such as minimum CPI, completion of specific coursework and prerequisites, the degree program, etc. will be determined by individual DAU faculty mentor.
Internship Structure and Duration
- The SRI Program will typically run for a period of eight weeks between May and July. Specific dates each year will be determined by a faculty mentor.
- Interns are expected to commit to a minimum of 40 hours per week to their research project, as agreed upon with their faculty mentor.
- The research project will typically require the student to engage in various aspects of the research process, such as literature review, data collection, data analysis, and potentially manuscript preparation or presentation.
Stipend and Housing
- The intern shall receive a stipend of Rs. 1000 per week for a maximum of eight weeks.
- The intern shall reside on the DAU campus. The hostel accommodation charges shall be waived.
- The Institute will provide a travel allowance of up to INR 3000. The selected candidate will need to submit the original receipts/tickets of travels to/from DAU during the internship period.
- Meals and other expenses shall be the responsibility of the intern.
Application Process
- Interested students shall directly submit a formal application to the DAU faculty mentor through email.
- The application shall include:
- The student’s resume or curriculum vitae highlighting their academic achievements and relevant experiences.
- A statement of interest outlining their research interests, relevant coursework, and reasons for applying to the specific internship(s).
- No objection certificate from their parent Institute.
- Optionally, letters of recommendation.
Summer Internship Projects 2025 @ DAU
| Faculty Name | Project Name | Project Details | |
|---|---|---|---|
| Prof. Abhishek Jindal | Reinforcement learning based optimisation of LLMs | With the success of DeepSeek, it is imperative to study RL for optimising LLMs. The work involves reviewing recent publications in the area and developing a working model. | abhishek_jindal[at]daiict[dot]ac[dot]in |
| Prof. Abhishek Jindal | Reinforcement learning for high frequency trading | The integration of RL and LSTM can offer significant improvement in high frequency trading systems. The work involves developing a working system in this domain. | abhishek_jindal[at]daiict[dot]ac[dot]in |
| Prof. Abhishek Jindal | Graph neural networks for cybersecurity | GNNs can help in fraud and spam detection. The work involves studying GNNs in the broader context of cybersecurity. | abhishek_jindal[at]daiict[dot]ac[dot]in |
| Prof. Biswajit Mishra | Digital IC Design | ASIC Design using Digital CMOS | biswajit_mishra[at]daiict[dot]ac[dot]in |
| Prof. Biswajit Mishra | Embedded Hardware and Software and App Development for Healthcare Applications | Project involves working on further developing the app and extending an already existing embedded system for healthcare application. | biswajit_mishra[at]daiict[dot]ac[dot]in |
| Prof. Pankaj Kumar | Solar Harvesting | Solar harvesting using metasurface design | pankaj_kumar[at]daiict[dot]ac[dot]in |
| Prof. Vinay S Palaparthy | Sensor interface electronics | To design generic board to detect change in resistance/capacitance of the VOC sensor | vinay_shrinivas[at]daiict[dot]ac[dot]in |
| Prof. Vinay S Palaparthy | Fabrication of RRAM/Memristors | To fabricate the Memristors on the flexible substrates | vinay_shrinivas[at]daiict[dot]ac[dot]in |
| Prof. Vinay S Palaparthy | Nanomaterial synthesis | Synthesis of 2D nanomaterials for agriculture applications | vinay_shrinivas[at]daiict[dot]ac[dot]in |
| Prof. Yash Vasavada | Development of a machine learning-based framework for real-time anomaly detection in electricity consumption data | Collect the electricity consumption data from existing electricity utility companies and available datasets worldwide, and develop advanced analytical tools using machine learning and deep learning for obtaining the decisions regarding anomalous usage patterns including fraudulent activities | yash_vasavada[at]daiict[dot]ac[dot]in |
| Prof. Yash Vasavada | MIMO Precoding Schemes for Next Generation Wireless Communication Systems | In this project, the summer intern will focus on MIMO algorithms and detection mechanisms for advancing the state of the art of wireless communications | yash_vasavada[at]daiict[dot]ac[dot]in |
| Prof. Sanjay Srivastava Prof. Manish Chaturvedi |
Modelling and Simulation of Walk and Intelligent Feeder Service Based Last Mile Connectivity to Public transport | Last mile connectivity refers to the critical link that connects commuters from their homes or workplaces to the nearest public transportation hubs. In this project, an intelligent feeder service is to be designed to enhance last mile connectivity to public transport. An online algorithm is to be developed to recommend suitable boarding junctions for commuters to join shared rides. Reference Link |
sanjay_srivastava[at]daiict[dot]ac[dot]in |
| Prof. Sanjay Srivastava Prof. Manish Chaturvedi |
Two-wheeler driving pattern analysis using mobile phone sensors | The Advanced Driver Assistance System (ADAS) in cars is used to improve the safety of drivers. However, limited or no ADAS is available for two-wheeler (2W) and three-wheeler (3W) vehicle drivers who represent 80% of the driver population in India. This project aims to design a wearable device / mobile phone based vehicle independent driver assistance system. Reference Link |
sanjay_srivastava[at]daiict[dot]ac[dot]in |
| Prof. Tapas Kumar Maiti | Embedded AI/ML | Integration of artificial intelligence (AI) and machine learning (ML) algorithms directly into low-cost embedded systems—small-scale, resource-constrained devices such as microcontrollers, sensors, or edge devices. Instead of sending data to the cloud for processing, these systems can analyze data locally in real time. | tapas_kumar[at]daiict[dot]ac[dot]in |
| Prof. Tapas Kumar Maiti | Robotics and Automation Systems | Robotics and automation technologies create powerful, autonomous systems capable of operating efficiently and securely at the edge. Key tools include ROS, TensorFlow Lite, TinyML, and platforms like NVIDIA Jetson and ESP32. | tapas_kumar[at]daiict[dot]ac[dot]in |
| Prof. Tapas Kumar Maiti | IoT and Edge Computing with End-to-End Solution | IoT connects devices to collect and exchange real-time data, while Edge Computing processes this data locally, reducing latency and bandwidth use. Together, they enable faster decision-making and optimize operations. These devices are used across industries like smart homes, healthcare, and manufacturing. | tapas_kumar[at]daiict[dot]ac[dot]in |
| Prof. Sudip Bera | On the domination number of various algebraic graphs | The examination of graphs associated with various algebraic structures has gained significance in the past twenty years. The study of these graphs offers multiple advantages. Firstly, it allows us to categorize the resulting graphs. Secondly, it enables us to identify algebraic structures that possess isomorphic graphs. Lastly, it helps us understand the interdependence between algebraic structures and their corresponding graphs. One of the most important graph parameter is domination number of graph. The domination number of a graph, representing the minimum number of vertices needed to "dominate" all other vertices, has applications in facility location, network design, and social network analysis. It helps optimize resource allocation, identify key individuals or nodes, and ensure network connectivity or coverage. In this project we want to determine the domination number of some algebraic graphs. | sudip_bera[at]daiict[dot]ac[dot]in |