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Postdoctoral Research Scholar

Job in Raleigh, Wake County, North Carolina, 27601, USA
Listing for: North Carolina State University
Full Time position
Listed on 2026-08-05
Job specializations:
  • Research/Development
    Research Scientist, Postdoctoral Research Fellow, Data Scientist, Biomedical Science
Job Description & How to Apply Below

Postdoctoral Research Scholar

The Department of Physics and Astronomy at NC State University seeks to fill the position for a Postdoctoral Research Scholar 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.

Duties and 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. Depending on the scholar's background, the research may emphasize bimolecular simulation, AI for bimolecular science, or an integrated combination of both.

Responsibilities will include computational method development, rigorous validation against experimental data, development of reproducible software and workflows, manuscript preparation, and dissemination of research findings. 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.

Other Work/Responsibilities
- 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 and Preferences

Work Schedule - 40 hours/week, Monday-Friday

Department Required Skills
- 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) AI4

Science, 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 are required. 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…

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