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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 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 is a Physics Informed Machine Learning job?

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.

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Infographic showing various Physics Informed Machine Learning job openings in Washington as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
2026 PhD Graduate - Artificial Intelligence and Complex Systems Postdoc Researcher

2026 PhD Graduate - Artificial Intelligence and Complex Systems Postdoc Researcher

The Johns Hopkins University Applied Physics Laboratory

Laurel, MD • On-site

$115K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Description

Are you eager to use artificial intelligence (AI) to unlock insights from complex, high-dimensional data towards national security impact?

Do you want to bring innovative AI to real-world challenges in domains such as materials science, biology, chemistry, and advanced manufacturing?

Are you pursuing studies in AI, machine learning, or a related field - and looking for a Postdoc role where you can push the boundaries of AI for Science in interdisciplinary and high-impact ways?

If so, we'd love to meet you!

We are seeking a creative Postdoc problem solver with strong technical capabilities and initiative to join our team in taking on challenges in complex systems. The Complex Systems Group, part of the Intelligent Systems Center (www.jhuapl.edu/isc), conducts research at the intersection of AI and complex systems, with an emphasis on advancing capabilities in machine learning-based surrogate models, scientific automation, AI-assisted reasoning, and AI-guided design. We develop methods to drive discovery and design across scientific and engineering domains towards national security impact. This may include AI research in materials, protein engineering, chemistry, earth systems, and other physical and biological systems.

As a Postdoc researcher in our group, your primary responsibility will be contributing to and leading AI for Science projects with national security implications. As a member of our research team, you will contribute to AI for Science projects by creating, adapting, and evaluating modern AI methods including machine learning models, frontier AI systems, agentic workflows, physics- and domain-informed learning, AI-driven surrogate models, and closed-loop discovery architectures for scientific and engineering problems. You will establish collaborations with science and engineering domain experts and seek opportunities to propose your ideas for funding. You will also establish working relationships with our Program Managers to identify opportunities to grow our work in key areas for our sponsors. You will publish your research in impactful venues and engage with the external research community, external collaborators, and sponsors.


Qualifications

You meet our minimum qualifications for the job if you:

  • Have a PhD degree in a relevant field such as AI, Mathematics, Statistics, Computer Science, Data Science, or Engineering.

  • Have published previous research in peer-reviewed journals and/or conferences.

  • Have experience in one or more key technical skills, including modern AI/ML methods, deep learning, foundation models, agentic workflows, physics- or domain-informed ML, AI-driven surrogate modeling, diffusion modeling, neural operators, graph neural networks, large language models, or symbolic AI.

  • Are proficient in Python and developing AI/ML software.

  • Have experience with containerized, cloud, High Performance Computing (HPC), or distributed computing environments.

  • Are able to obtain an Interim Secret security clearance by your start date and can ultimately obtain a Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You'll go above and beyond our minimum requirements if you:

  • Have background knowledge in a science or engineering discipline such as materials, chemistry, biology, or advanced manufacturing.

  • Are skilled in project management or leading technical teams.

  • Have experience writing technical proposals, particularly for government research sponsors.

  • Have experience applying AI/ML to real-world data such as remote sensing data, materials data, omics data, or additive manufacturing related data.

  • Have experience with relevant Python-based AI and scientific computing tools, such as PyTorch, JAX, or related libraries.


About Us

Why Work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities athttps://www.jhuapl.edu/careers.

All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law.APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contactAccessibility@jhuapl.edu.

The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.


Minimum Rate
$85,300 Annually
Maximum Rate
$155,500 Annually

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