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Bayesian Phd Jobs in Boston, MA (NOW HIRING)

Lila Sciences

Co-Op, Autonomous SEM

Cambridge, MA · On-site

Currently pursuing a PhD or have completed a PhD in Materials Science, Chemistry, Chemical ... Experience with closed-loop learning, active learning, Bayesian optimization, or reward-driven ...

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Bayesian Phd information

What is a Bayesian PhD?

A Bayesian PhD typically refers to an individual who has completed a doctoral program with a focus on Bayesian statistics or Bayesian methods in their research. Bayesian statistics is a branch of statistics that uses probability distributions to represent uncertainty about unknowns, updating beliefs as new data becomes available. Students in this field learn to develop and apply Bayesian models to a wide range of problems in science, engineering, and social sciences. A PhD program with a Bayesian focus often involves advanced coursework in probability theory, statistical inference, and computational methods, as well as original research using Bayesian approaches.

What are the key skills and qualifications needed to thrive as a Bayesian PhD?

To thrive as a Bayesian PhD, you need advanced knowledge of probability theory, statistical inference, and mathematics, typically supported by a doctoral degree in statistics, mathematics, or a related field. Proficiency with statistical programming languages like R, Python, and specialized Bayesian tools such as Stan or BUGS is essential. Strong critical thinking, problem-solving, and clear communication skills help in articulating complex analyses and collaborating across disciplines. These capabilities are crucial for developing rigorous models, conducting impactful research, and translating statistical insights into actionable solutions.

What are some common challenges faced by a Bayesian PhD researcher during collaborative projects?

Bayesian PhD researchers often collaborate with interdisciplinary teams, which can present challenges such as communicating complex statistical concepts to non-specialists and integrating Bayesian methods with other analytical frameworks. Balancing the depth of theoretical work with practical problem-solving, managing computational demands, and aligning project goals with collaborators' expectations are also common hurdles. Successful collaboration typically requires strong communication skills, adaptability, and a willingness to bridge methodological gaps between disciplines.

What is the difference between Bayesian Phd vs Data Scientist?

AspectBayesian PhdData Scientist
Required CredentialsPhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, academic or specialized industry rolesBusiness-focused, tech companies, or consulting firms
Industry UsageAcademic research, advanced analytics, specialized modelingData analysis, machine learning, business insights
Common Search/ComparisonYesYes

While a Bayesian PhD specializes in advanced statistical modeling and research, a Data Scientist applies data analysis and machine learning techniques in practical business contexts. Both roles require strong analytical skills, but the Bayesian PhD typically focuses on theoretical development, whereas the Data Scientist emphasizes application and implementation.

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What job categories do people searching Bayesian Phd jobs in Boston, MA look for?

The top searched job categories for Bayesian Phd jobs in Boston, MA are:

What cities near Boston, MA are hiring for Bayesian Phd jobs?

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Bayesian Phd jobs near you
Infographic showing various Bayesian Phd job openings in Boston, MA as of August 2026, with employment types broken down into 1% Internship, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

AI Residency Program, Material Science (2026 Cohort)

Lila Sciences

Cambridge, MA • On-site, Remote

Full-time

Re-posted 10 days ago

Key responsibilities

  • Design and execute independent research projects in AI for materials science

  • Collaborate with Lila scientists and engineers on open-science initiatives

  • Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation

Job description

AI Resident - 2026 Cohort

The AI Residency Program is a full-time research opportunity designed to bridge the gap between academic research and industry applications in AI for materials science. Residents will work closely with Lila scientists and engineers on high-impact, open-science projects, with the option to focus on either fundamental or applied research.

  • Duration: 6-12 months (extension possible)
  • Start Dates: First hires beginning January 2026, with rolling applications and additional intakes in Summer and Fall 2026
  • Cohort Size: Small group of selected residents
  • Mentorship: Pairing with technical mentors, feedback from cross-functional teams
  • Resources: Access to proprietary datasets, high-performance compute, and Lila's research infrastructure

Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation.

 
Application Requirement:
Please submit your resume alongside a research proposal (up to 3 pages, unlimited references) outlining the project you would plan to pursue during your residency at Lila Sciences. Please submit your research proposal as your cover letter. Applications without both documents will not be considered. Optional supporting materials (e.g., recommendation letters, publications, research artifacts) may also be included. 

Your Impact at Lila

The Lila Sciences AI Residency is a full-time research program at the intersection of artificial intelligence and materials science. As a resident, you'll join a cohort of researchers tackling open-ended scientific challenges alongside Lila's world-class team of scientists and engineers. With access to proprietary datasets, high-performance compute infrastructure, and experienced mentors, you'll pursue ambitious research projects with both academic and real-world impact. Publishing is encouraged but not required - what matters most is pushing the frontier of scientific discovery.

What You'll Be Building

  • Design and execute independent research projects in AI for materials science
  • Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
  • Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
  • Contribute to collaborative team research and co-develop novel approaches to scientific discovery
  • Share findings internally and externally; publications are welcome but not mandatory

What You'll Need to Succeed

  • Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor's, Master's, or PhD)
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch)
  • Experience working with large-scale datasets or simulations
  • Familiarity with modern AI/ML architectures and training techniques
  • Strong research background, demonstrated through publications, thesis work, or open-source projects

Bonus Points For

  • Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
  • Familiarity with Bayesian optimization, active learning, or generative models
  • Experience in reinforcement learning or agent-based approaches to scientific reasoning
  • Open-source contributions or collaborative research experience
  • Strong communication and writing skills, especially for conveying complex scientific ideas

Apply