Vice President Computational Structural Biology Jobs

The Associate Computational Scientist will assist laboratory personnel in computational studies aimed at elucidating the kinetic and thermodynamic mechanisms by which ligands with varying efficacies, including positive allosteric modulators (PAMs) regulate the activation and signaling of G protein-coupled receptors (GPCRs), with particular emphasis on the -opioid receptor. The research combines long-timescale molecular dynamics simulations, adaptive sampling, enhanced sampling techniques, and Markov state modeling (MSMs) to characterize transient conformational states and quantify ligand-dependent transition pathways that are inaccessible to experimental structural biology alone. The project integrates computational structural biology with cryo-electron microscopy to determine how allosteric modulators alter receptor activation kinetics, signaling efficacy, and receptor-transducer interactions. These mechanistic insights will guide the rational discovery and optimization of novel PAMs that enhance therapeutic efficacy while minimizing adverse effects, thereby accelerating the development of safer analgesics and other GPCR-targeted therapeutics. The position will also contribute to (a) the development of generative deep learning frameworks for GPCR dynamics that infer collective variables and conformational landscapes from molecular simulations, enabling efficient sampling of receptor activation pathways and predictive modeling of signaling kinetics, and (b) the training and application of large language models using real-world data.

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• Masters degree or equivalent in a domain science; Ph.D. in a scientific domain preferred.
• Beginner level, with some experience in a scientific/academic computing environment or equivalent preferred.

Preferred Skills

• Ph.D. in Computational Biophysics, Computational Chemistry, Computational Biology, Bioinformatics, Biophysics, or a related quantitative discipline. 
• Demonstrated expertise in molecular dynamics simulations of membrane proteins, and especially G Protein Coupled Receptors. 
• Advanced expertise in Markov state modeling, kinetic modeling of biomolecular systems, transition path theory, and analysis of long-timescale molecular simulation data. 
• Experience with adaptive sampling strategies and enhanced sampling methods, including metadynamics, OPES, umbrella sampling, or related algorithms. 
• Experience integrating computational simulations with experimental structural or biophysical data, including cryo-EM, spectroscopy, or single-molecule experiments. 
• Experience in computational drug discovery, protein-ligand interactions, and structure-based design of allosteric modulators. 
• Proficiency in Python and scientific computing libraries, Linux/Unix systems, GPU computing, and high-performance computing environments. 
• Experience in the development, training, fine-tuning, and evaluation of large language models or other foundation models for biomedical or healthcare applications.
• Strong publication record demonstrating independent development of computational methodologies for biomolecular systems. 
• Excellent written and oral communication skills and the ability to work collaboratively in multidisciplinary research teams.

• Masters degree or equivalent in a domain science; Ph.D. in a scientific domain preferred.
• Beginner level, with some experience in a scientific/academic computing environment or equivalent preferred.

Preferred Skills

• Ph.D. in Computational Biophysics, Computational Chemistry, Computational Biology, Bioinformatics, Biophysics, or a related quantitative discipline. 
• Demonstrated expertise in molecular dynamics simulations of membrane proteins, and especially G Protein Coupled Receptors. 
• Advanced expertise in Markov state modeling, kinetic modeling of biomolecular systems, transition path theory, and analysis of long-timescale molecular simulation data. 
• Experience with adaptive sampling strategies and enhanced sampling methods, including metadynamics, OPES, umbrella sampling, or related algorithms. 
• Experience integrating computational simulations with experimental structural or biophysical data, including cryo-EM, spectroscopy, or single-molecule experiments. 
• Experience in computational drug discovery, protein-ligand interactions, and structure-based design of allosteric modulators. 
• Proficiency in Python and scientific computing libraries, Linux/Unix systems, GPU computing, and high-performance computing environments. 
• Experience in the development, training, fine-tuning, and evaluation of large language models or other foundation models for biomedical or healthcare applications.
• Strong publication record demonstrating independent development of computational methodologies for biomolecular systems. 
• Excellent written and oral communication skills and the ability to work collaboratively in multidisciplinary research teams.

Compensation Statement

The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $72,473.00 - $108,709.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.

Non-Bargaining Unit, 858 - Pharmacological Sciences - ISM, Icahn School of Medicine

• Provide computational expertise to laboratory personnel in the development, implementation, and application of adaptive-sampling molecular dynamics workflows for membrane protein simulations on high-performance computing platforms.
• Construct, validate, and interpret MSMs to quantify receptor activation pathways, free-energy landscapes, transition kinetics, and metastable conformational states. 
• Perform transition path theory analyses, mean first-passage time calculations, kinetic network analyses, and free-energy estimation to characterize ligand-dependent signaling mechanisms. 
• Provide computational expertise to laboratory personnel in the design and execution of enhanced sampling protocols, including metadynamics, OPES (On-the-fly Probability Enhanced Sampling), umbrella sampling, and related approaches to investigate rare conformational events. 
• Model receptor-ligand, receptor-G protein, and receptor-allosteric modulator interactions using molecular docking, molecular dynamics simulations, and statistical mechanical analyses. 
• Design and evaluate positive allosteric modulators through structure-based computational drug discovery, virtual screening, and quantitative analysis of ligand efficacy. 
• Develop and train large language models using de-identified clinical and biomedical datasets, including electronic health records, biomedical literature, and structured knowledge bases, to enable clinical decision support, biomedical question answering, and scientific knowledge extraction.
• Develop reproducible computational pipelines using Python, Linux, version control systems, and GPU-enabled high-performance computing environments. 
• Contribute to manuscripts, grant applications, software documentation, and presentations describing computational methods and research discoveries.

Expected outcomes of the project include:

• Identification of previously uncharacterized intermediate conformational states and ligand-dependent activation pathways of the -opioid receptor and related GPCRs. 
• Quantitative kinetic and thermodynamic models describing receptor activation, signaling, and allosteric modulation. 
• Novel computational methodologies for adaptive sampling, enhanced sampling, Markov state modeling, and machine learning-based analysis of biomolecular dynamics. 
• Structure-based identification and optimization of positive allosteric modulators with improved therapeutic potential and reduced adverse effects. 
• Advance the development of a large language models under development in the lab for applications to GPCR target identification and drug discovey, enabling more accurate, scalable, and interpretable analysis of complex biomedical and clinical data.
• Peer-reviewed publications, publicly available computational tools and workflows, and preliminary data supporting future extramural grant applications. 
• Collectively, these outcomes will advance the fundamental understanding of GPCR activation mechanisms while accelerating the development of safer analgesics and other GPCR-targeted therapeutics through computationally guided drug discovery.