Postdoctoral Research Scholar

Raleigh, NC

About the role

The Department of Physics and Astronomy at NC State University seeks to fill a Postdoctoral Research Scholar position in the Lin Research Group. The scholar will advance computational studies of bimolecular structure, dynamics, interactions, and functions. The initial appointment is for one year, with possible renewal for a second year contingent upon satisfactory performance.

In the cover letter, candidates should briefly describe the project that best demonstrates their relevant expertise, their independent contributions to method, algorithm, or software development, and the connection of this work to one or more of the position’s research directions.

Responsibilities

• Predictive modeling and design of protein–nucleic acid interactions, including protein–DNA recognition, protein–RNA interactions, and protein- or small-molecule binding to oligonucleotide aptamers, in close collaboration with experimental groups.
• Physics-based and AI-enabled modeling of chromatin organization, RNA conformational dynamics, and RNA–protein phase separation.
• Conduct independent and collaborative computational research on bimolecular structure, dynamics, interactions, and functions. Develop, implement, and evaluate molecular simulation and/or deep-learning methods; construct and analyze atomistic or coarse-grained models; integrate structural, sequence, thermodynamic, and experimental data; and perform rigorous statistical and mechanistic analyses. Research may address protein–DNA and protein–RNA recognition, aptamer binding and design, RNA conformational ensembles and dynamics, RNA–protein phase separation, and chromatin organization.
• Maintain reproducible, well-documented software and workflows, communicate findings through peer-reviewed publications, conference presentations, and research software, and collaborate closely with experimental and computational research partners.
• Participate in group meetings and cross-laboratory collaborations; mentor graduate and undergraduate researchers; contribute to manuscripts, conference presentations, grant proposals, outreach activities, and research software and documentation; and help develop new research directions.

Requirements

• Ph.D. or equivalent terminal degree in physics, chemistry, biophysics, computational biology, bioinformatics, computer science, applied mathematics, or a closely related field, conferred before the employment start date.
• Demonstrated research expertise in at least one of two tracks: (1) biomolecular simulation, including advanced modeling and analysis, force-field or coarse-grained model development, and/or enhanced-sampling methods; or (2) AI4Science, particularly the development or rigorous adaptation of deep-learning methods for biomolecular prediction or design. Applicants in the AI track should have a strong foundation in algorithms and quantitative methods and be able to analyze, adapt, and evaluate machine-learning approaches rather than use them solely as black-box tools.
• Strong scientific programming, communication, and collaborative skills.
• Knowledge of molecular simulation and/or machine learning; biomolecular structure and dynamics; statistical mechanics or data-driven modeling; scientific programming; HPC / GPU workflows; data analysis and visualization; reproducible research; and scientific communication.
• Ability to formulate research questions, develop and benchmark methods, diagnose model or simulation failures, interpret results mechanistically, manage projects independently, and collaborate across disciplines.
• Ability to write papers and mentor students. Grant writing experience is a plus.

Preferred Qualifications

• Candidates whose research integrates AI with physics, chemistry, or biology are particularly encouraged to apply.
• Experience with proteins, nucleic acids, protein-DNA or protein-RNA interactions, aptamers, RNA structure and dynamics, biomolecular phase separation, chromatin, or epigenetics.
• Experience with molecular-dynamics packages such as OpenMM, GROMACS, NAMD, or LAMMPS; Python and/or C/C++; GPU / HPC computing; and deep-learning frameworks such as PyTorch.
• A strong record of peer-reviewed publications, evidence of independent method or algorithm development, and well-documented research software.
• Particular preference for applicants who have developed deep-learning approaches for DNA / RNA prediction or design, or advanced simulation methods for complex biomolecular systems.

Benefits

• Medical, Dental, Vision, Retirement and Leave
• Faculty and Staff Assistance Program
• LEAD courses for career enhancement
• Free attendance at non-revenue generating sporting events
• Childcare benefits, Wellness & Recreation Membership, and Wellness Programs

Pay

$58,656–$70,000

Schedule

40 hours/week, Monday–Friday