Connectionists: Open PhD position in Neuromorphic AI Lab

[Dhireesha Kudithipudi]

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:

1. Cover letter describing your motivation for applying to this position (1
paragraph)

2. CV and unofficial academic transcripts (with grades if applicable)

Qualifications and requirements:
1. Master's degree, or equivalent, in a discipline related to Electrical &
Computer Engineering, computer science, computational neuroscience,
physics, and related fields.
2. Background and/or strong interest in developing skills in artificial
intelligence, computer architecture, machine learning, quantitative
methods, and computer arithmetic.
3. Knowledge in programming, preferably in Python. Additional knowledge
preferred in deep learning software (Tensorflow/TensorRT, Pytorch, Keras or
similar).
4. 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.
5. Good written and verbal communication skills are essential.
6. A collaborative spirit and the ability to work as part of an
interdisciplinary team are essential.
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