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Physics Informed Machine Learning Jobs in Allston, MA

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Lead Research Scientist - Machine Learning

Woburn, MA · On-site

$174K - $220K/yr

... physics, and data science. We use our expertise and creativity to take innovative ideas from ... Experience adapting novel machine learning approaches (e.g., from academic literature) to new data ...

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How much do physics informed machine learning jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for physics informed machine learning in Allston, MA is $21.89, according to ZipRecruiter salary data. Most workers in this role earn between $13.65 and $27.79 per hour, depending on experience, location, and employer.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

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What job categories do people searching Physics Informed Machine Learning jobs in Allston, MA look for?

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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Allston, MA as of August 2026, with employment types broken down into 6% Internship, 42% Full Time, 44% Part Time, and 8% Contract. Highlights an 100% In-person job distribution, with an average salary of $45,536 per year, or $21.9 per hour.

Staff - Postdoctoral Research Fellow - AI for Science

Mitsubishi Electric Research Laboratories

Cambridge, MA • On-site, Remote

$135K - $170K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 10 days ago

Job description

MERL is seeking a highly motivated Postdoctoral Research Fellow in AI for Science to join our research team. This position offers a distinctive opportunity for early-career researchers to pursue independent, curiosity-driven research at the intersection of artificial intelligence and scientific discovery, within a collaborative industrial research lab that values academic-style innovation and publication.

The successful candidate will have the freedom to define and lead a research agenda aligned with MERLs strategic interests in applying AI to challenging problems arising in industrial and scientific domains. This includes advancing foundational methods while demonstrating impact in real-world systems.

We are particularly interested in candidates working at the intersection of AI and scientific domains, including but not limited to:

AI for Mathematics and Scientific Computing

  • AI-driven methods for optimization, numerical analysis, and large-scale scientific computing

  • Learning-based approaches to modeling and analyzing complex dynamical systems

  • Integration of machine learning with classical algorithms for improved efficiency and robustness

  • Hypothesis formation, sequential experiment design, analysis and deductive discovery.

  • Theorem development and proving, and algorithm discovery.

AI for Physical Sciences

  • Physics-constrained or physics-informed AI models for understanding and designing material properties (e.g., optical, electrical, thermal)

  • Data-driven and hybrid approaches for multi-scale modeling and simulation

  • Differentiable simulators

  • Discovery of emergent laws and governing equations in complex physical systems

  • Foundational models for thermodynamics and electro-magnetics

AI for Earth and Environmental Sciences

  • Methods for multi-scale environmental and geophysical systems

  • Remote sensing and spatio-temporal modeling using satellite or sensor data

  • Modeling, prediction, and decision-making for environmental and climate-related applications

Responsibilities for this position include:

  • Define and pursue an independent research program in AI for Science, aligned with MERLs research directions
  • Develop novel machine learning and AI methodologies for scientific discovery, modeling, and design
  • Collaborate with MERL researchers across disciplines to connect fundamental advances with industrial applications
  • Publish research results in top-tier conferences and journals
  • Contribute to the broader research community through open-source software, datasets, or other artifacts where appropriate

Qualifications for this position are:

  • PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field (or expected completion before start date)
  • Strong background in machine learning, artificial intelligence, and computational science
  • Experience in applying AI to scientific or engineering domains, particularly those domains described above
  • Demonstrated ability to conduct independent research, as evidenced by a strong publication record in relevant venues
  • Excellent communication skills and ability to collaborate in a multidisciplinary research environment

The pay range for this position at the commencement of employment is expected to be between $135,000 and $170,000 per year. Actual base pay will depend on a variety of individualized factors, including job-related knowledge, skills, and experience.

In addition to base salary, the total compensation package will include an annual performance-based bonus and a comprehensive benefits program encompassing medical, dental, and vision coverage; a generous 401(k) match; commuting and home office stipends and various paid time off programs (including vacation, sick time, personal and parental leave). Specific details regarding participation in these programs will be provided upon an offer of employment.

Employment is considered at-will, and the Company reserves the right to modify base salary or any other compensation program at any time, including for reasons related to individual performance, departmental or Company performance, and market conditions.

PRINCIPALS ONLY. No phone calls please.

Mitsubishi Electric Research Laboratories, Inc. is an Equal Opportunity Employer.

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL's employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.

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