Connectionists: Open PhD position in Brain-Inspired Artificial Intelligence at the Neuromorphic AI Lab

[Dhireesha Kudithipudi]

Open PhD position in *Brain-Inspired Artificial Intelligence* at the
Neuromorphic
AI Lab, University of Texas at San Antonio

Preferred Start Date: 01/01/2022 (Flexible)

Deadline for full consideration: 11/30/2021

We are seeking a Ph.D. student to join an exciting new research project
designing brain-inspired models in continual learning scenarios funded by
agencies such as NSF, DARPA and AFRL. Specifically, the candidate is
expected to develop and study neural network models using techniques
inspired by neuroscience concepts such as consolidation, neurogenesis,
metaplasticity, and neuromodulation. The project will involve close
interaction with our 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 within the newly established UTSA AI
consortium (MATRIX). 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 can be found at  CVPR-W, ICML-W, DATE, IEEE
Signal Processing Magazine, 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 computer
science, computational neuroscience, information processing and/or machine
learning.
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 in
deep learning software (Tensorflow/TensorRT, Pytorch, Keras or similar) is
desirable.
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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