Interesting stuff! It would be interesting to see whether large language models could also help explain certain strategies or interface with traditional financial techniques. For instance, you might be able to have the language model interface with traditional portfolio optimization techniques using something like toolformer or differentiable portfolio optimizers (https://github.com/cvxgrp/cvxpylayers).
Location: UK (Edinburgh preferred and London/elsewhere in UK is possible)
Remote: No Preference
Willing to relocate: can relocate to US starting May 2024
Technologies: deep learning (pytorch python and C++ api), combinatorial optimization, django, react, linux
Email: aferber6174@gmail.com
Résumé/CV links:
Enthusiastic upcoming CS PhD grad from USC who has published papers on applying machine learning to combinatorial optimization problems with applications in finance, wildlife trafficking, illegal fishing, recommendation systems, inverse photonics, and cloud cybersecurity. Eager to work on new application domains and have dabbled in tuning stable diffusion and LLaMA. During internships in Meta AI Research, NEC Labs, and Microsoft Azure, I have developed novel machine learning techniques for industrial scale domains.
I am moving to Edinburgh Scotland with my partner who will be working as a junior doctor there and am looking for positions that can help me grow and where I can leverage my experience in computer science + operations research for interdisciplinary work.
