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Physics Informed Machine Learning Jobs in Tennessee

Deloitte

AI Solutions Architect

Nashville, TN · On-site

$60.75 - $80.25/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... or Physics, or equivalent experience * 8+ years of experience in product sales, software ...

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Physics Informed Machine Learning information

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.

What are popular job titles related to Physics Informed Machine Learning jobs in Tennessee? For Physics Informed Machine Learning jobs in Tennessee, the most frequently searched job titles are:
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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Tennessee as of June 2026, with employment types broken down into 1% Locum Tenens, 84% Full Time, 11% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution.
Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets

Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets

Oak Ridge National Laboratory

Oak Ridge, TN • On-site

Full-time

Posted 14 days ago

Oak Ridge National Laboratory rating

9.3

Company rating: 9.3 out of 10

Based on 15 frontline employees who took The Breakroom Quiz

3rd of 103 rated laboratories

Job description

Job Summary:
Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security. They are seeking an outstanding Postdoctoral Research Associate with a strong background in condensed-matter physics and materials science to develop AI models for accelerated materials discovery, particularly focusing on permanent magnets.
Responsibilities:
• Work closely with members of NTI and CNMS to develop new AI models for discovering novel permanent magnets with targeted properties using advanced concepts such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc.
• Design, develop, and validate interpretable cross-modal AI/ML models incorporating features from electronic structure theory for predictive structure-chemistry-property discovery in magnetic solids and validate them against multi-modal experimental measurements
• Perform high-throughput first-principles electronic structure calculations (e.g. DFT and post-DFT methods) for generating datasets to train AI models leveraging DOE’s HPC platforms
• Develop new methodologies that can describe both atomic and spin relaxation accurately but at a much cheaper computational cost than DFT
• Present and report research results and publish in peer-reviewed journals in a timely manner
• Ensure compliance with environment, safety, health, and quality program requirements
• Maintain a strong commitment to the implementation and perpetuation of values and ethics
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success
Qualifications:
Required:
• A PhD in Condensed Matter Physics, Materials Science, Chemistry, Physics, or a closely related science discipline completed within the last five years
Preferred:
• A demonstrated record of developing advanced physics-informed AI models for scientific discovery
• Hands-on expertise developing and applying machine learning for materials and/or process discovery, particularly quantum materials
• Some form of expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab initio / experimental dataset or machine-learning tight-binding DFT methods
• Expertise in using or developing generative tools for automation of scientific discovery
• Expertise in using high-performance computing (HPC) platforms for delivering breakthrough scientific results
• A record of productive and creative research proven by publications in peer-reviewed journals and/or conference presentations
• Excellent written and oral communication skills
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs
Company:
Oak Ridge National Laboratory holds a range of R&D assignments, from fundamental nuclear physics to applied R&D on advanced energy systems. Founded in 1943, the company is headquartered in Oak Ridge, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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