Postdoctoral Associate

Stony Brook University • Stony Brook, NY

20h • Onsite

About The Position

The research group of Prof. Ivet Bahar, Director of the Laufer Center for Physical & Quantitative Biology at Stony Brook University, invites applications for a Postdoctoral Research Associate to join a program of work at the interface of structural dynamics, allostery, and computational drug discovery. The lab's guiding principle — “Bridging Structure & Function, via Dynamics” — investigates how the intrinsic flexibility and collective motions of biomolecules govern molecular recognition, allosteric communication, and functional adaptation across a wide range of disease-relevant systems. The successful candidate will use enhanced-sampling molecular dynamics, coarse-grained elastic network models, Markov state modeling, and machine-learning methods to characterize the conformational dynamics and activation mechanisms of signaling proteins and other therapeutic targets, and to translate these mechanistic insights into structure-based and rational drug discovery. There is also scope to contribute to the group's ongoing work connecting structural dynamics to functional and disease-variant prediction (e.g., the Rhapsody framework) and to the group's broader efforts developing and disseminating open-source computational tools such as ProDy.

Requirements

• A PhD (or foreign equivalent) in Computational Biophysics, Biochemistry, Chemistry, Bioinformatics, or a closely related quantitative field in hand by the start of the appointment.
• Demonstrated expertise in molecular dynamics simulation and enhanced-sampling techniques (e.g., Gaussian accelerated MD/GaMD, ligand GaMD).
• Experience building and interpreting Markov State Models or comparable approaches for identifying metastable states and conformational transitions.
• Proficiency in Python and standard molecular simulation/analysis tools (e.g., AMBER, VMD, PyMOL, Chimera, Cpptraj).
• A track record of first-author publications in peer-reviewed journals, and strong written and oral communication skills.

Nice To Haves

• Experience in structure-based drug discovery workflows, including virtual screening, molecular docking (e.g., AutoDock Vina, Schrödinger), and free-energy/ADMET analysis.
• Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation.
• Familiarity with machine learning and deep learning methods (e.g., variational autoencoders) applied to biomolecular dynamics data.
• Familiarity with elastic network modeling (ANM/GNM) or the ProDy software package.
• A track record of independent or co-led research projects and international conference presentations.

Responsibilities

• Investigate the conformational dynamics and allosteric activation mechanisms of signaling proteins and other disease-relevant targets (e.g., kinases, receptors, transporters) using classical and enhanced-sampling molecular dynamics (e.g., GaMD, LiGaMD) and coarse-grained elastic network models (ANM/GNM).
• Apply Markov State Models and machine-learning approaches (e.g., autoencoders, dimensionality reduction) to identify metastable conformational states and map activation and allosteric pathways.
• Conduct virtual screening, molecular docking, free-energy calculations, and ADMET profiling to support structure-based and rational design of small-molecule inhibitors and drug repurposing efforts.
• Contribute mechanistic, dynamics-based insight to one or more of the group's active research directions, including allostery, molecular recognition, and structure-based prediction of the functional impact of disease-associated variants.
• Collaborate with the group's network of experimental and computational partners to validate theoretical predictions.
• Co-author manuscripts for submission to leading structural biology, biophysics, and computational chemistry journals, and present research at group meetings, seminars, and international conferences.
• Contribute to the intellectual life of the Laufer Center (e.g., seminar series, mentoring of graduate/undergraduate students) as opportunities arise.
• Other duties as assigned.

Benefits

• The Research Foundation of SUNY policies and procedures
• Sponsor guidelines
• Availability of funding
• FLSA Exempt position, not eligible for the overtime provisions of the FLSA.
• Minimum salary threshold must be met to maintain FLSA exemption.
• Total rewards

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree