Research Scientist - Cambridge, United States - Massachusetts Institute of Technology

Massachusetts Institute of Technology Cambridge, United States

2 weeks ago

Description

REQUIRED:
Ph.
D.

in computer science, mathematics, or related field or a master's and five years' experience, ideally through at least one postdoctoral appointment; ability to work effectively in a highly variable, dynamic, and unstructured environment across all levels with faculty, staff, students, and other MIT community members; and attention to detail.

PREFERRED:

industry systems software engineering experience; experience with probabilistic programming, probabilistic modeling, and Monte Carlo approximate inference; mathematical maturity at the intersection of computation, geometry, and probability; experience with systems software development and engineering practices, including git-based version control, written requirements capture, software design documentation, unit and regression testing, and agile software development; track record of academic PL research in venues such as NeurIPS, AISTATS, PLDI, and POPL, or a graduate degree in theoretical math and systems software engineering experience; and experience collaborating with graduate student researchers.

Job #22261

Must be able to work occasional evenings and/or weekends.

1/10/23

RESEARCH SCIENTIST, Brain and Cognitive Sciences /Quest for Intelligence/Computer Science and Artificial Intelligence LabProbabilistic Computing Project (multiple openings), to perform fundamental research on probabilistic computation, probabilistic programming, and their applications.

Will mentor and advise students/postdocs/other research scientists, improve open-source software, support the teaching of probabilistic programming within and outside of MIT, lead and contribute to research and publications, and present at conferences.

Responsibilities include contributing to artificial intelligence and natural intelligence research that leverages hybrids of neural, symbolic, and probabilistic computation; implementing probabilistic programs, making improvements to probabilistic programming systems, and/or developing and analyzing mathematical models of probabilistic programming languages; contributing to quantitative evaluations of AI systems, models of natural intelligence, probabilistic programming platforms, and research planning and reporting; improving open-source software platforms; and supporting the broad dissemination and teaching of probabilistic programming platforms via workshops and MIT academic courses.

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