Cross-Caps Lab, Infosys Centre for AI @IIIT Delhi, India, in joint collaboration with JP Morgan & Chase (JPMC), is inviting applications for the IIITD-JPMC Postdoctoral Fellow position in the field of Machine learning or Deep Learning for acoustic and/or language modeling. The fellowship will be tenable at IIIT Delhi and will be offered for a period of 1 year in the first instance. Fellowship is expected to result in high-quality publication in a top-tier conference/journal.

For general queries, reach out to abrol[at]iiitd[dot]ac[dot]in with the subject "Inquiry for IIITD Postdoctoral Fellowship".

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

Timelines and Application Procedure:
Applications are accepted through this Google form. https://lnkd.in/e_m9kMHUThe last date for application is 1st September 2022!
The position will start in September 2022 (or soon after).
Applications will be evaluated constantly and filled on as soon as a suitable candidate is found.Applications around the globe are welcomed.

Benefits:
- Fellowship of ​INR 80K - 90K per month.
- Research contingency and international travel support.
- Access to high-performance computing infrastructure.
- Networking opportunity with peers and collaborators from IIITD and JPMC.
- The position will be offered for one year in the first instance and will be extended based on performance.

Responsibilities
- Fellows must contribute to several aspects of the research lifecycle, spanning ideation, implementation, and experimentation.- High-quality publications in a top-tier conference/journal.

​Qualifications

You must hold a Ph.D. degree with CGPA>7.5 in CSE/ECE/Mathematics or related disciplines with CGPA>7.5 at the time of application. Candidates who have submitted their thesis but are yet to defend it or those who are expected to submit it in the next two months are also encouraged to apply.

Your Ph.D. work will be able to demonstrate knowledge in Machine Learning/AI/ASR/NLP and experience with sequence-to-sequence based Deep Learning technologies (e.g., Transformers, LSTM, NMT). Proven experience in designing, implementing, and optimizing End-to-End training and inference speech technology systems, including but not limited to modern and cutting-edge language/acoustic modeling, multi-lingual/cross-language ASR, TTS, model compression & acceleration, speaker diarization, and voice separation. Experience with libraries and models, e.g., Kaldi, SpeechBrain, BERT, OpenAI GPT, OpenNMT, CoreNLP, NLTK, Word2vec, and GloVe. Good publication track records in conferences and journals, including, but not limited to TASLP, TNN, INTERSPEECH, ICASSP, NeurIPS, ASRU, ICLR, and ACL. Good knowledge of Python and hands-on experience with TensorFlow/PyTorch.