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Bayesian Phd Jobs in Massachusetts (NOW HIRING)

QuEra Computing, Inc.

Photonics Engineer

Boston, MA · On-site

$128K - $208K/yr

Master's or PhD in Electrical Engineering, Physics, or a related field, or 4+ years of experience ... Applied ML or AI for device design or layout optimization, such as inverse design, Bayesian ...

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 ...

QuEra Computing, Inc.

Photonics Engineer

Boston, MA · On-site

$128K - $208K/yr

Master's or PhD in Electrical Engineering, Physics, or a related field, or 4+ years of experience ... Applied ML or AI for device design or layout optimization, such as inverse design, Bayesian ...

Open Data Science Conferenc

Lead Data Scientist

Boston, MA · On-site

$152 - $217/hr

PhD in a quantitative field (PhD strongly preferred). * Strong foundation in statistics and applied ... Bayesian methods, regularization, and optimization is a plus * Experience with AI (e.g., NLP/LLMs ...

New

MMS Holdings Inc

Study Design Statistician (US)

Boston, MA · On-site

$120 - $180/hr

... PhD preferred. * 5‑7 years of experience in the application of medical statistics (pharma, CRO ... Experience of Bayesian approaches to design and analysis of clinical data preferred. * Experience ...

MMS

Study Design Statistician (US)

Boston, MA · On-site

... PhD preferred. * 5-7 years of experience in the application of medical statistics (pharma, CRO ... Experience of Bayesian approaches to design and analysis of clinical data preferred. * Experience ...

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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.

What are popular job titles related to Bayesian Phd jobs in Massachusetts?

For Bayesian Phd jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Bayesian Phd jobs in Massachusetts look for?

The top searched job categories for Bayesian Phd jobs in Massachusetts are:

What cities in Massachusetts are hiring for Bayesian Phd jobs?

Cities in Massachusetts with the most Bayesian Phd job openings:

Bayesian Phd jobs near you
Infographic showing various Bayesian Phd job openings in Massachusetts as of August 2026, with employment types broken down into 76% Full Time, 19% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution.

Director, Materials AISF Program Lead

Lila Sciences

Cambridge, MA • On-site

Full-time

Posted 28 days ago

Job description

Your Impact at LILA

We are looking for a Program Lead to own strategy and execution for Lila's Materials Science AISF program. This role defines how and when the program delivers against Product, Science, and AI priorities, then turns that strategy into clear charters, budgets, timelines, and execution plans.

Materials Science AISFs span vacuum synthesis, characterization, electrochemistry, nanoporous materials, and physics. The Program Lead will connect deep domain expertise, platform strategy, and cross-functional execution to expand the experimental systems that help AI learn materials science at scale.

This role reports to John Gregoire and partners closely with Product, AISF Engineering, Materials Experiment, Scientific Programs, and technical leadership. Success requires clear strategy, decisive execution, and the ability to align teams around complex, high-impact platform priorities.

What You'll Be Building

  • Own the Materials Science AISF program strategy, charter, budget, timeline, and success criteria.
  • Translate Product, Science, and AI priorities into executable program plans.
  • Build the roadmap for expanding Materials Science AISF capabilities across synthesis, characterization, and closed-loop AI guidance.
  • Evaluate proof-of-concept results from Scientific Programs and design pathways for platform integration.
  • Form, align, and advise cross-functional project teams executing the Materials Science AISF strategy.
  • Resolve resourcing, prioritization, and execution risks in partnership with Product and portfolio leads.
  • Represent Materials Science AISF strategy, status, risks, and decisions in technical leadership reviews.

What You'll Need to Succeed

  • PhD or equivalent experience in materials science, condensed matter physics, chemistry, or a closely related field, with industry research experience.
  • Track record leading complex, multidisciplinary experimental programs.
  • Familiarity with Materials Science AISF domains such as vacuum synthesis, characterization, electrochemistry, or nanoporous systems.
  • Experience defining and managing technical roadmaps with real resource, budget, and timeline constraints.
  • Strong written and verbal communication, with the ability to translate technical depth into strategic clarity.
  • Experience leading through influence across matrixed, cross-functional teams.

Bonus Points For

  • Experience with automated or high-throughput experimental platforms, robotics, or lab automation.
  • Familiarity with AI/ML-driven experimental design, Bayesian optimization, or closed-loop materials discovery.
  • Exposure to atomistic simulation methods and their relationship to experimental programs.
  • Experience in a fast-paced research environment such as a national lab, advanced materials startup, or deep-tech company.
  • Experience working across shared resources and multiple concurrent programs.

Apply