Our research aims to develop new foundations and technologies for reasoning and planning that leverage large language models (LLMs) and integrate them with complementary approaches, including program synthesis, and causal and probabilistic programming. The goal is to create integrated systems that harness the deep domain knowledge and emergent capabilities of LLMs, alongside the precision, efficiency and correctness of formal reasoning frameworks. This could deliver the best of both worlds: the intuitive, context-rich insights of LLMs and the logical, structured analysis provided by formal methods. Key application areas include developing integrated agents that:

Automate data science: augmenting and automating functionalities in the data science pipeline, such as data curation, cleaning, format translation, causal and statistical analysis, and model verification.
Reliably execute long-term plans using heterogeneous external tools: improving automated planning and decision-making in complex open environments.

Postdoctoral Fellows will have the opportunity to develop these tools within concrete projects with real-world impact, leveraging the collective networks of Basis, Harvard, and Hugh Kaul Precision Medicine Institute. Specifically, postdoctoral fellows will be able to contribute to larger initiatives to (i) dramatically advance precision medicine, or (ii) advance society's capacity to make informed civic policy decisions, building upon Basis work in civic policymaking.

Core Responsibilities:
Conduct independent and collaborative research, focusing on reasoning and language models
Apply these methods to concrete applications within precision medicine or civic policymaking
Disseminate research findings through academic publications and presentations at leading conferences.
Actively engage in knowledge transfer within Basis and Harvard, converting research into actionable insights and algorithms.
Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.

The research environment is both structured and adaptable, with multiple avenues for scholarly contribution. As a postdoctoral fellow, your expertise can shape various aspects of the project. It will deepen your foundations in multiple domains, including large language models, reasoning, planning, and precision medicine or civic policymaking while also allowing for a balance of focused research, academic exploration, and software development.

Role Details
Full-time: This position is full-time and has a fixed duration of 1 to 2 years.
Location: Applicants must reside in, or be willing to relocate to, the Greater Boston area. This is an in-person position — you will have space at both Nada Amin's lab and at Basis. You will be expected to travel periodically, about once every six to eight weeks, for Basis-wide in-person events, typically in New York.

Researchers holding a PhD related to programming languages, artificial intelligence, and machine learning. Researchers with experience in other adjacent technical areas such as physics or mathematics will also be considered.
Individuals with a demonstrated track record in scientific research, which can be evidenced through publications, technical reports, or impactful software projects.

Experience in machine learning, particularly with (i) language models and (ii) reinforcement learning and planning, is highly valued.
Interest in end-user applications, especially in precision medicine or policymaking.

Prof. Nada Amin

Please include a cover letter and research statement.

SEAS is dedicated to building a diverse and welcoming community.

Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.