IIITD-IITD Research Fellowship (for current M.Tech/MSR students)

[Vinayak Abrol]

Cross-Caps Lab, Infosys Centre for AI @IIIT Delhi in joint collaboration with IIT Delhi is inviting applications from graduate students currently enrolled in the Master's program in education/research institutes in India to avail of fellowship for research work at IIITD. There is a range of areas which fellows can explore, including, but not limited to problems in theoretical deep learning, visualizations for representation learning, transfer learning, explainability in AI, federated learning, etc. The fellowship will be tenable at IIIT Delhi and if selected the student applying for this grant must provide an endorsement from his parent institute before joining. Fellowship is expected to result in a thesis with optional publication in a top-tier conference/journal.

For general queries, interested candidates can send an email to abrol[at]iiitd[dot]ac[dot]in with the subject "Inquiry for IIITD-IITD Research Fellowship".

Cross-Caps Lab: https://bit.ly/38UqDk1

Timelines and Application Procedure:
Applications are accepted through this Google form.

https://lnkd.in/dW2-4FPe
The last date for application is 30th July!
The position will start on 1st August 2022 (or soon after).
Applications will be evaluated constantly and will be filled on as soon as a suitable candidate is found.

The selection process takes into consideration academic performance, coding skills, aptitude for research, and software development.

Benefits:
- Fellowship of ​INR 30K = (25K + 5K performance bonus) per month
- The position will be offered for 6 months in the first instance and extended for 6 months based on performance or till the completion of the thesis.
- Access to high-performance computing infrastructure.
- Networking opportunity with peers and collaborators from IIITD, IITD, and abroad.

Responsibilities
- Fellows must contribute to several aspects of the research lifecycle, spanning ideation, implementation, and experimentation.
- This research work is expected to result in a thesis with optional publication in a top-tier conference/journal.
- A fellow is expected to work full-time at IIITD without any academic/non-academic commitments from his parent institute during the duration of this fellowship.

​Qualifications
You will be able to demonstrate knowledge of popular machine learning and deep learning techniques and practical coding experience with libraries such as TF/Pytorch. A solid foundation in topics such as linear algebra, probability, signal processing, etc. is a must. You must hold a bachelor's degree with CGPA>7.5 and currently enrolled in a Master's program in CSE/ECE/Mathematics or related disciplines with CGPA>7.5 at the time of application.