Job Description

We are hiring postdoctoral fellows interested in advancing the state of the art in data-efficient machine learning (AutoML, Bayesian optimization, neural architecture search, meta-learning, active 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 and research assistants will be investigating how these concepts (as well as important issues imvolving AI privacy, model auditability and updatability) can be applied in the context of LLMs, among others.

• Research on topics related to data-efficient machine learning, such as AutoML, Bayesian Optimization, neural architecture search, data valuation, meta-learning, and active learning
• 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 have the opportunity to collaborate with the PhD and undergraduate students in our research groups.

Some preliminary efforts can be found here:

https://transformers.mit.edu/ https://xqlin98.github.io/INSTINCT/ http://arxiv.org/abs/2310.00646

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

• Ph.D. in computer science, computer engineering, machine learning, statistics, math, data science, operations research or other related disciplines.
• Good publication record in the premier machine learning and AI conferences and/or journals is preferred. 
• Strong proficiency in programming.

To apply, please visit our website at: https://portal.smart.mit.edu/careers/career-opportunities

Interested applicants are invited to send in their

• detailed CV with academic ranking (if any) and publication list,
• a short cover letter describing your suitability for the position,
• a concise description of research interests and future plans,
• academic transcripts and
• list of three references (to include reference names and contact information).

We regret that only shortlisted candidates will be notified.