Postdoctoral Associate

Job Description - Postdoctoral Associate (2603070)

Postdoctoral Associate

• 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.

• Experience in structure-based drug discovery workflows, including virtual screening, molecular docking (e.g., Auto Dock 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 Pro Dy software package.
• A track record of independent or co-led research projects and international conference presentations.

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 Pro Dy.

The Bahar group develops and applies multiscale computational approaches — from coarse-grained elastic network models (ANM/GNM) to atomistic and enhanced-sampling molecular dynamics and machine learning/AI methods — to understand how biomolecules achieve diverse functions through flexible, dynamic structures. This work spans allosteric signaling, molecular recognition, and structure-function relationships, and supports the discovery of rational therapeutic strategies against cancer, neurological disorders, and drug-resistant infectious diseases.

The group maintains an active, internationally collaborative research portfolio with experimental partners and is based at the Laufer Center for Physical & Quantitative Biology, a highly interdisciplinary research environment at Stony Brook University.

• 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…