We are seeking a Ph.D. student to join in an exciting new research project — designing bio-inspired AI models for lifelong learning scenarios– funded by multiple agencies. Specifically, the candidate is expected to study and develop algorithms with theoretical formulations. A successful candidate will be able to propose novel algorithmic approaches and provide strong mathematical proof for the former, towards solving challenges/addressing knowledge gaps in lifelong learning. The successful candidate will also be part of a rich and emerging AI community, within the newly established UTSA AI consortium (MATRIX). The MATRIX consortium engages with the private sector, academia, and the greater San Antonio community to advance the state of the art in human-aware AI.
Qualifications and requirements:
- Master’s degree, or equivalent, in a discipline related to computer science, computer engineering, statistics, information processing, computational neuroscience, and/or machine learning.
- Background and/or strong interest in developing skills in statistical analysis, mathematics for machine learning, quantitative methods, probability theory.
- Knowledge in programming, preferably in Python or R or Java, understanding of data structures, data modeling ( SQL ) and software architectures.
- Familiarity in deep learning frameworks and libraries is preferred(Tensorflow/TensorRT, Pytorch, Keras, or similar) and data analysis libraries ( Pandas, numpy, matplotlib, scikit-learn, seaborn, etc).
- Willingness to learn and write robust code using new frameworks or programming languages based on the project requirements.
- The successful candidate will be expected to design and perform independent research and publish papers in refereed top conferences and journals.
- Candidates should keep abreast of the latest developments in the field and develop strong research aptitude.
- Good written and verbal communication skills are essential.
- A collaborative spirit and the ability to work as part of an interdisciplinary team are essential.
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)