Computational Modeling Simulation Multiphysics Jobs in Kearny, NJ

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.

Strength through Unity and Inclusion

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai's unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.

At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.

About the Mount Sinai Health System:

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time - discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients' medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report's "Best Children's Hospitals" ranks Mount Sinai Kravis Children's Hospital among the country's best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek's "The World's Best Smart Hospitals" ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.

Equal Opportunity Employer

The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.

• 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.