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Postdoctoral In Reinforcement Learning Jobs in Massachusetts

Tufts University

Postdoctoral Scholar

Medford, MA · On-site

$67K/yr

Overview Postdoctoral Scholar in Machine Learning for Physical Systems The Department of Electrical and Computer Engineering at Tufts University invites applications for a Postdoctoral Scholar in the ...

Klaviyo

Senior AI Engineer - Customer Agent

Boston, MA · On-site

$113K - $155K/yr

Experience in reinforcement learning. We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the ...

CVS Health

Senior Machine Learning Engineer

Wellesley, MA · On-site

$111K - $222K/yr

Strong background in one or more of the following: reinforcement learning, causal inference, LLM, MCP * Experience as a mentor or tech lead * Ability to work and influence cross-functionally with ...

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Postdoctoral In Reinforcement Learning information

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in Massachusetts?

For Postdoctoral In Reinforcement Learning jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Massachusetts look for?

The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Massachusetts are:

What cities in Massachusetts are hiring for Postdoctoral In Reinforcement Learning jobs?

Cities in Massachusetts with the most Postdoctoral In Reinforcement Learning job openings:

Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Massachusetts as of June 2026, with employment types broken down into 47% Full Time, 38% Part Time, 5% Temporary, 5% Contract, and 5% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution.

Postdoctoral Scholar

Tufts University

Medford, MA • On-site

$67K/yr

Full-time

Re-posted 8 days ago

Tufts University rating

8.4

Company rating: 8.4 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

103rd of 621 rated colleges and universities

Job description

Overview
Postdoctoral Scholar in Machine Learning for Physical Systems
The Department of Electrical and Computer Engineering at Tufts University invites applications for a Postdoctoral Scholar in the research group of Prof. Peter Lu. The position focuses on machine learning methods for physical systems, including ML emulators or surrogate models for chaotic dynamics and materials modeling. The successful candidate will develop and analyze ML models for scientific problems and will have latitude to shape the research direction in collaboration with the PI
What You'll Do
Relevant application domains include high-dimensional PDEs, turbulence, and materials modeling. Methodological interests in the group span scientific generative modeling, representation learning, optimal transport, and neural operators. Candidates whose expertise connects to any of these areas, and who want to work at the interface of rigorous physical modeling and modern ML are encouraged to apply.
Responsibilities include conducting independent and collaborative research, developing and validating scientific ML models and code, disseminating results through publications and presentations, and contributing to grant activity and the mentoring of student researchers.
What We're Looking For
Required qualifications: a PhD (completed or expected before the start date) in electrical and computer engineering, physics, applied mathematics, computer science, or a closely related field; a strong record of research; and proficiency in scientific computing and modern ML frameworks.
Preferred qualifications: demonstrated experience in one or more of PDEs, dynamical systems, turbulence, materials modeling, or ML surrogate models; familiarity with differentiable programming; and a background spanning both physical modeling and machine learning.
The preferred start date is as soon as possible. The appointment is for up to two years: an initial one-year term with a second year contingent on performance and funding. Salary follows the Tufts compensation schedule for postdoctoral scholars and is commensurate with experience.
To apply, please submit a CV, a brief statement of research interests, and two confidential letters of reference. Review of applications will begin immediately and continue until the position is filled. Inquiries may be directed to Prof. Peter Lu (peter.lu@tufts.edu).
Pay Range
Minimum $67,500.00, Midpoint $67,500.00, Maximum $67,500.00
Salary is based on related experience, expertise, and internal equity; generally, new hires can expect pay between the minimum and midpoint of the range.

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