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Postdoctoral Research Associate, Atomistic Simulations & AI-Driven Molecular Modeling (Oak Ridge, TN, US, 37830) @ Oak Ridge National Laboratory

Oak Ridge, Tennessee, USOnsiteFull-time
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<p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Requisition Id 16217 </span></p><p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>­­Overview: </strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>Key Responsibilities:</strong></span></p> <ul> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Develop and apply scalable molecular dynamics (MD) and multiscale simulation workflows for biomolecular systems (proteins, enzymes, membranes, and complexes)</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Contribute to cross-scale modeling frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis)</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Optimize simulation codes and workflows for leadership-class HPC architectures</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Collaborate across interdisciplinary teams spanning biology, chemistry, computer science, and applied mathematics</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Publish findings in high-impact journals and present at leading conferences</span></li> </ul> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>Required Qualifications:</strong></span></p> <ul> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Strong programming skills in C++, Python, or similar scientific computing languages</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Hands-on experience with MD simulation tools such as NAMD, GROMACS, AMBER, or LAMMPS, and visualization tools (e.g., VMD, PyMOL)</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience working on high-performance computing (HPC) systems</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Demonstrated ability to conduct independent research with a good publication record</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Excellent written and verbal communication skills for interdisciplinary collaboration</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Commitment to ORNL’s core values: Impact, Integrity, Teamwork, Safety, and Service</span></li> </ul> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong> </strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>Preferred Qualifications:</strong></span></p> <ul type="disc"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Deep expertise in atomistic and multiscale simulation methods (e.g., MD, enhanced sampling, QM/MM)</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience improving performance and scalability of simulation workflows via:</span></li> <ul type="circle"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Parallelization and performance engineering</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">GPU/accelerator optimization</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Algorithmic innovation</span></li> </ul> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience applying machine learning or AI to molecular simulation, including:</span></li> <ul type="circle"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Surrogate models or learned potentials</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Generative models for biomolecular design</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Representation learning for biomolecular systems</span></li> </ul> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Familiarity with protein–protein interaction (PPI) networks, signaling pathways, or systems biology models (bioinformatics tools and models)</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience with integrated multiscale modeling frameworks connecting molecular dynamics to cellular or tissue-scale processes</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience with deep learning frameworks such as PyTorch or TensorFlow</span></li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving foundation models or scientific LLMs</span></li> </ul> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong> </strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>Special Requirements: </strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.<strong> </strong></span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>Security, Credentialing, and Eligibility Requirements:</strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black"><strong>About ORNL:</strong></span></p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: [email protected] </span></p><p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt">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.</span></p> <p> </p> <p><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt">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.</span></p> <p><br><span style="font-family:arial, helvetica, sans-serif;font-size:10.0pt">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.</span></p>

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