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Physics Informed Machine Learning Jobs in Williamsburg, VA

... informed decision-making on resource management issues at all levels of government, including ... Experience integrating AI or machine learning approaches into environmental modeling * Experience ...

APPRENTICE

Newport News, VA · On-site

$50K - $74K/yr

Algebra, Algebra 2, Geometry, Advanced Mathematics, Chemistry, Physics, Mechanical Drawing ... HII's diverse workforce includes skilled tradespeople; artificial intelligence, machine learning ...

Chief of Staff

Newport News, VA · On-site

$120 - $166/hr

... Machine Learning and Software Engineering - enabling data and tech-enabled solutions that deliver ... support informed decision-making and organizational transparency * Assess enterprise resource ...

New

... Machine Learning and Software Engineering - enabling data and tech-enabled solutions that deliver ... support informed decision-making and organizational transparency * Assess enterprise resource ...

... Machine Learning and Software Engineering - enabling data and tech-enabled solutions that deliver ... support informed decision-making and organizational transparency * Assess enterprise resource ...

... IoT, machine learning, and artificial intelligence. In an increasingly connected world, massive ... advise your clients as they make informed decisions. Ultimately, you'll provide a deep ...

Physics Informed Machine Learning information

See Williamsburg, VA salary details

$5

$19

$24

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

As of Aug 20, 2026, the average hourly pay for physics informed machine learning in Williamsburg, VA is $19.24, according to ZipRecruiter salary data. Most workers in this role earn between $11.97 and $24.42 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.

What are popular job titles related to Physics Informed Machine Learning jobs in Williamsburg, VA?

For Physics Informed Machine Learning jobs in Williamsburg, VA, the most frequently searched job titles are:

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

The top searched job categories for Physics Informed Machine Learning jobs in Williamsburg, VA are:

What cities near Williamsburg, VA are hiring for Physics Informed Machine Learning jobs?

Cities near Williamsburg, VA with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Williamsburg, VA as of June 2026, with employment types broken down into 1% Internship, 74% Full Time, 17% Part Time, 2% Temporary, and 6% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $40,011 per year, or $19.2 per hour.

Research Scientist

Williammary

Gloucester Point, VA

$65K/yr

Full-time

Re-posted 17 days ago


Job description

Job Requisition:

JR101439 Research Scientist (Open)

Job Posting Title:

Research Scientist

Department:

CC00496 VIMS1 | RADV | CCRM

Job Family:

Staff - Scientist

Worker Sub-Type:

Fixed Term (requires end date - benefited) (Fixed Term)

Job Requisition Primary Location:

Virginia Institute of Marine Science

Primary Job Posting Location:

Virginia Institute of Marine Science

Job Description Summary:

The Virginia Institute of Marine Science (VIMS) has a three-part mission of research, education, and advisory service. Our overarching goals are to:
-Make seminal advances in understanding marine systems through research and discovery
-Translate research findings into practical solutions to complex issues of societal importance
-Provide new generations of researchers, educators, problem solvers, and managers with a coastal and marine sciences education of unsurpassed quality
The Center for Coastal Resources Management (CCRM) supports informed decision-making on resource management issues at all levels of government, including private and corporate stakeholders, through applied research, advisory services, and scientific innovation.

Job Description:

The Research Scientist will join the Coastal Systems Modeling Group within CCRM to support and advance research in coastal hydrodynamics, water quality processes, and next-generation operational modeling systems. The position will contribute to the development and implementation of seamless "creek-to-ocean" modeling frameworks using the SCHISM modeling system and advance artificial intelligence (AI)-based modeling approaches for coastal flooding and estuarine systems.

The Research Scientist will work collaboratively with faculty, graduate students, visiting scholars, and interdisciplinary partners while also developing independent research aligned with programmatic priorities and sponsor objectives.

Required Qualifications

Education

  • Ph.D. in Coastal Engineering, Oceanography, Civil/Environmental Engineering, Marine Science, Applied Mathematics, or closely related field

Experience

  • Demonstrated record of peer-reviewed research and scholarship commensurate with career stage

  • Experience with numerical modeling of oceanic and estuarine systems

  • Experience using unstructured grid models, particularly SCHISM

  • Experience working in interdisciplinary research teams

Knowledge, Skills & Competencies

  • Strong quantitative and analytical skills

  • Advanced programming skills (e.g., Python, Fortran, MATLAB, R, C/C++)

  • Experience with web-based model applications or visualization tools

  • Knowledge of coastal and estuarine physical processes

  • Familiarity with uncertainty quantification and statistical analysis

Preferred Qualifications

  • Experience integrating AI or machine learning approaches into environmental modeling

  • Experience with operational forecasting systems

  • Experience working on NOAA-funded or federal research projects

  • Experience mentoring graduate students

  • Experience contributing to proposal development and grant management

Salary Range: Starting at $65,000.00

Core Duties & Responsibilities

1. Coastal Systems Modeling & Numerical Simulation (35%)

  • Develop, implement, and refine unstructured-grid hydrodynamic and water quality models, particularly using the SCHISM modeling framework

  • Support development of seamless creek-to-ocean operational forecasting systems

  • Conduct model calibration, validation, and performance evaluation

  • Perform uncertainty quantification and statistical analyses of model outputs

  • Maintain version control and reproducible modeling workflows

2. AI-Based Model Development & Innovation (25%)

  • Develop and extend next-generation AI and machine learning models for coastal flooding and estuarine processes

  • Integrate AI methodologies with physics-based numerical models

  • Explore hybrid modeling frameworks to improve predictive skill and computational efficiency

  • Contribute to advancing operational forecasting tools for stakeholder use

3. Research, Scholarship & Grant Support (20%)

  • Contribute to peer-reviewed publications, technical reports, and sponsor deliverables

  • Assist in preparation of grant proposals and continuation applications

  • Present research findings at scientific conferences, workshops, and stakeholder meetings

  • Support NOAA-funded research objectives and reporting requirements

4. Collaboration & Mentorship (15%)

  • Work closely with graduate students, postdoctoral associates, and visiting scientists

  • Participate in interdisciplinary research teams

  • Engage with collaborators across institutions and agencies

  • Provide technical guidance on modeling workflows and computational methods

5. Independent Research Development (5%)

  • Develop new research directions aligned with coastal systems modeling goals

  • Identify emerging funding opportunities

  • Contribute to strategic growth of the modeling program

Conditions of Employment

  • Position is grant-funded and term-restricted

  • Continued employment contingent upon funding availability and performance

  • Occasional travel for conferences or collaborator meetings may be required

  • Occasional evening or weekend work to meet project deadlines may be required

Additional Job Description:

Job Profile:

JP0126 - Scientist II - Exempt - Salary - S10

Qualifications:

Compensation Grade:

S10

Recruiting Start Date:

2026-03-03

Review Date:

Position Restrictions:

Restricted Funds & Restricted Term

EEO is the Law. Applicants can learn more about William & Mary's status as an equal opportunity employer by viewing the "Know Your Rights" poster published by the U.S. Equal Employment Opportunity Commission. https://www.eeoc.gov/know-your-rights-workplace-discrimination-illegal

Background Check: William & Mary is committed to providing a safe campus community. W&M conducts background investigations for applicants being considered for employment. Background investigations include reference checks, a criminal history record check, and when appropriate, a financial (credit) report or driving history check.

Remote Work Disclaimer: Remote work eligibility is not guaranteed and is subject to approval. Employee eligibility depends on the likelihood of the employee succeeding in a remote work arrangement and the supervisor's ability to manage remote workers. Departments and/or Human Resources may amend, alter, change, delete, or modify eligibility.