Postdoctoral Fellow-MSH-30040-226

Mount Sinai Health System • New York, NY

5d

About The Position

Dr. Kleiman’s anticipated research will expand and strengthen the Filizola lab’s ongoing computational efforts to advance our understanding of membrane protein conformational dynamics and activation kinetics, with a particular focus on G protein–coupled receptors (GPCRs) and their therapeutic modulation. Key responsibilities will include: (a) Performing enhanced molecular dynamics simulations to investigate GPCR structure, dynamics, and activation mechanisms, integrating AI-enhanced simulation strategies and Markov state models to elucidate kinetic selection processes; (b) Leading in-depth computational analyses of GPCRs and other membrane proteins to define critical structure–function relationships; (c) Contributing to generative modeling and deep-learning efforts aimed at predicting compounds with defined bioactive properties, supporting the lab’s innovation in AI-driven drug discovery; (d) Developing and implementing novel computational methodologies that advance the lab’s scientific objectives and inform experimental design; (e) Rigorously analyzing and interpreting complex datasets to generate mechanistic insights into receptor signaling and activation; (f) Preparing preliminary data for grant applications and contributing to the drafting of manuscripts for publication in high-impact scientific journals; and (g) Working collaboratively within the research team, mentoring graduate students, and fostering an inclusive, supportive research environment that promotes intellectual growth and collaboration.

Requirements

• Molecular dynamics simulations
• Machine Learning
• Algorithm Design
• Protein Structure Prediction
• Protein Language Models & Conformational Ensemble Prediction
• Cheminformatics
• HPC & Cloud Scaling
• Parallel computation
• PhD in Chemistry, Biophysics, Computational Biology, machine learning, or related discipline
• PhD-level research training

Responsibilities

• Performing enhanced molecular dynamics simulations to investigate GPCR structure, dynamics, and activation mechanisms, integrating AI-enhanced simulation strategies and Markov state models to elucidate kinetic selection processes
• Leading in-depth computational analyses of GPCRs and other membrane proteins to define critical structure–function relationships
• Contributing to generative modeling and deep-learning efforts aimed at predicting compounds with defined bioactive properties, supporting the lab’s innovation in AI-driven drug discovery
• Developing and implementing novel computational methodologies that advance the lab’s scientific objectives and inform experimental design
• Rigorously analyzing and interpreting complex datasets to generate mechanistic insights into receptor signaling and activation
• Preparing preliminary data for grant applications and contributing to the drafting of manuscripts for publication in high-impact scientific journals
• Working collaboratively within the research team, mentoring graduate students, and fostering an inclusive, supportive research environment that promotes intellectual growth and collaboration

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees