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Physics Informed Machine Learning Jobs in Tennessee

NGA AI Engineer Manager

Nashville, TN · On-site

$73K - $244K/yr

They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...

They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...

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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 cities in Tennessee are hiring for Physics Informed Machine Learning jobs? Cities in Tennessee with the most Physics Informed Machine Learning job openings:
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, Atomistic Simulations & AI-Driven Molecular Modeling

Postdoctoral Research Associate, Atomistic Simulations & AI-Driven Molecular Modeling

Oak Ridge National Laboratory

Oak Ridge, TN

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 10 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

Requisition Id 16217 

­­Overview:  

The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) section of the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate.

This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling of complex biosystems. The successful candidate will also contribute to efforts that bridge molecular, cellular, and systems-level modeling, with growing relevance to emerging paradigms such as whole-cell modeling and networked biological systems.

You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing resources and collaborating across ORNL, federal agencies, and academic partners.

Key Responsibilities:

  • Develop and apply scalable molecular dynamics (MD) and multiscale simulation workflows for biomolecular systems (proteins, enzymes, membranes, and complexes)
  • Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity
  • Contribute to cross-scale modeling frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis)
  • Optimize simulation codes and workflows for leadership-class HPC architectures
  • Collaborate across interdisciplinary teams spanning biology, chemistry, computer science, and applied mathematics
  • Publish findings in high-impact journals and present at leading conferences

Required Qualifications:

  • Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field
  • Strong programming skills in C++, Python, or similar scientific computing languages
  • Hands-on experience with MD simulation tools such as NAMD, GROMACS, AMBER, or LAMMPS, and visualization tools (e.g., VMD, PyMOL)
  • Experience working on high-performance computing (HPC) systems
  • Demonstrated ability to conduct independent research with a good publication record
  • Excellent written and verbal communication skills for interdisciplinary collaboration
  • Commitment to ORNL’s core values: Impact, Integrity, Teamwork, Safety, and Service

 

Preferred Qualifications:

  • Deep expertise in atomistic and multiscale simulation methods (e.g., MD, enhanced sampling, QM/MM)
  • Experience improving performance and scalability of simulation workflows via:
    • Parallelization and performance engineering
    • GPU/accelerator optimization
    • Algorithmic innovation
  • Experience applying machine learning or AI to molecular simulation, including:
    • Surrogate models or learned potentials
    • Generative models for biomolecular design
    • Representation learning for biomolecular systems
  • Familiarity with protein–protein interaction (PPI) networks, signaling pathways, or systems biology models (bioinformatics tools and models)
  • Experience with integrated multiscale modeling frameworks connecting molecular dynamics to cellular or tissue-scale processes
  • Experience with deep learning frameworks such as PyTorch or TensorFlow
  • Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving foundation models or scientific LLMs

 

Special Requirements: 

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Security, Credentialing, and Eligibility Requirements:

For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required.  Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation.  This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year.  This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov 

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.


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