Open PhD position in Energy Efficient Machine Learning at the Neuromorphic AI Lab, University of Texas at San Antonio

Preferred Start Date: 1/2/2022 (Flexible)
Deadline for full consideration: 11/30/2021

We are seeking a Ph.D. student to join an exciting new research project on designing energy-efficient models in continual learning scenarios funded by agencies such as NSF, DARPA and AFRL. Specifically, the candidate is expected to study lightweight deep neural network models, using multi-level model compression and optimization techniques. More often than not, these techniques are neuro-inspired. A successful candidate will interface closely with the hardware team to ensure that the designs are ready to deploy on edge devices. The successful candidate will also be part of a rich and emerging AI community, with the newly established UTSA AI consortium (MATRIX) community. The consortium engages with the private sector, academia, the Greater San Antonio community and international partners to advance the state of the art in human-aware AI.

The candidate will be mentored by Dr. Dhireesha Kudithipudi and often in collaboration with leading scientists in the field. Relevant recent publications from the lab are in venues such as CVPR-W, ICML-W, IJCAI-W, DATE, IEEE Signal Processing, IEEE TC.

How to Apply:

The position will remain open until filled. Applications can be submitted via email to Dr. Kudithipudi (dk at utsa.edu).

Applications should be submitted as a single PDF file:

• Cover letter describing your motivation for applying to this position (1 paragraph)
• CV and unofficial academic transcripts (with grades if applicable)

Qualifications and requirements:

• Master’s degree, or equivalent, in a discipline related to Electrical & Computer Engineering, computer science, computational neuroscience, physics, and related fields.
• Background and/or strong interest in developing skills in artificial intelligence, computer architecture, machine learning, quantitative methods, and computer arithmetic.
• Knowledge in programming, preferably in Python. Additional knowledge preferred in deep learning software (Tensorflow/TensorRT, Pytorch, Keras or similar).
• The successful candidate will be expected to design and perform independent research and publish papers in refereed top conferences and journals, through interdisciplinary research collaborations.
• Good written and verbal communication skills are essential.
• A collaborative spirit and the ability to work as part of an interdisciplinary team are essential.

Please click here to learn more.