To be eligible, an applicant must
- Be maximum 28 years of age
- Have a first-class master’s degree in Computer Science/Computer Science and Engineering/Information Technology/Mathematics and Computing (Major: Computer Science) or in related areas from any recognized University or Institute
- Have a valid GATE/NET score
- Have Strong programming skills, knowledge in the relevant area (natural language processing, machine learning, deep learning, programming with TensorFlow/PyTorch), and prior work experience in deep learning
The selected candidate will receive INR 31,000 per month plus 24% HRA (as per rules) per month for the first 2 years and INR 35,000 per month plus 24% HRA per month for the 3rd year (as per rules).
- A cover letter attaching a Curriculum Vitae (CV) including
- Father’s Name
- Date of Birth,
- Age and sex
- Mailing Address
- Telephone/Mobile Number, Email ID
- Academic qualifications starting from the 10th class
- Summary of experience, and SC/ST/OBC/differently-abled status with supporting documents)
- Soft-copies of mark sheets and certificates
How can you apply?
The eligible candidate can apply for the post by using the below-mentioned steps –
Step 1: Click on the “Apply Now”
Step 2: Send the cover letter with all the supporting documents to Dr. Debarshi Kumar Sanyal through email at – firstname.lastname@example.org on or before 6th May 2022.
Application Deadline – 6th May 2022
The candidate will be selected based on their performance in the interview.
Terms and Conditions
- Shortlisting will be done before the interview.
- No TA/DA will be paid for attending the interview.
- Only shortlisted candidates will be intimated by email and called for an interview.
- All applications must mention a valid email ID and phone number for communicating the details of the interview.
- The upper age limit is relaxable up to 5 years for SC/ST/OBC/differently-abled/Women candidates.
- All documents should be self-attested. Physical documents will be verified at the time of joining.
- The selection will be cancelled if discrepancies are found in the documents at the time of physical verification.