Project Overview

We are hiring postdoctoral fellows interested in advancing the state of the art in data-efficient machine learning at SMART in the new program: Mens, Manus and Machina: How AI empowers people and the city in Singapore (M3S) for a period of 1 year with possible renewal/extension. 

With the recent emergence of large language models (LLMs), the postdoctoral fellows will be investigating how data-efficient and resource-efficient techniques, such data attribution, data selection/reweighting, data valuation, data curation, Bayesian optimization, and retrieval-augmented generation, can be applied in the context of LLMs (as well as important issues involving AI privacy, model auditability and updatability).

Responsibilities

-Research on topics related to data-efficient machine learning in the context of LLMs, such as data attribution, data selection/reweighting, data valuation, data curation, and retrieval-augmented generation.

-Investigate how these concepts as well as important issues involving AI privacy, model audibility, and updatability can be applied in the context of LLMs. 

-Develop, implement, and evaluate experiments to characterise the feasibility and performance of the proposed research ideas. 

-Collaborate with other PhD and undergraduate students to publish research results in top-tier conferences and journals, with focus on venues associated with the above-mentioned areas. 

The postdoctoral fellows will be jointly advised by Prof. Daniela Rus (MIT CSAIL), Prof. Alex 'Sandy' Pentland (MIT Media Lab), and Assoc. Prof. Bryan Low (NUS School of Computing), and based at SMART (Singapore-MIT Alliance for Research & Technology) in Singapore. The postdoctoral fellows will have the opportunity to collaborate with PhD and undergraduate students, as well as other Postdoctoral Fellows, within our research groups.

For more information on our research group and interests, visit

https://danielarus.csail.mit.edu/

https://www.media.mit.edu/people/sandy/overview/

https://www.comp.nus.edu.sg/~lowkh/research.html

Requirements

-Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or other related disciplines.

-Strong publication record at premier AI/ML venues such as ICML, ICLR, NeurIPS, or similar. 

-Strong proficiency in programming. 

-Strong proficiency in English and communication skills. 

-Experience with machine learning and deep learning and LLM frameworks such as Pytorch, among others. 

Interested applicants are invited to send in their full CV/resume, cover letter and list of three references (to include reference names and contact information). We regret that only shortlisted candidates will be notified.