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Molecular Dynamics Simulations Postdoc Jobs (NOW HIRING)

NorthEastern

Postdoctoral Research Associate

Boston, MA · On-site

$60K - $85K/yr

The postdoc is also expected to write research papers, present research at meetings, contribute to ... defined), molecular dynamics simulations, sampling techniques, machine learning, or quantum ...

UNC-Chapel Hill

Open Rank

Chapel Hill, NC

... and molecular dynamics simulations) is required. The successful candidate will develop a research program characterizing how genetic variants of neuroreceptors modulate medication binding and ...

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Molecular Dynamics Simulations Postdoc information

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How much do molecular dynamics simulations postdoc jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for molecular dynamics simulations postdoc in the United States is $31.67, according to ZipRecruiter salary data. Most workers in this role earn between $27.16 and $35.82 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Molecular Dynamics Simulations Postdoc, and why are they important?

To thrive as a Molecular Dynamics Simulations Postdoc, you need a PhD in chemistry, physics, or a related field, with deep expertise in molecular modeling, statistical mechanics, and computational methods. Proficiency in simulation software such as GROMACS, AMBER, or LAMMPS, along with coding skills in Python, C++, or similar languages, is typically required. Strong problem-solving abilities, communication skills, and the capacity to work both independently and collaboratively help set candidates apart. These skills and qualities are critical for designing robust research, publishing impactful results, and advancing scientific understanding in highly technical environments.

What is a Molecular Dynamics Simulations Postdoc?

A Molecular Dynamics Simulations Postdoc is a researcher who has completed a PhD and works on projects involving computational simulations of molecular systems. Their work typically includes developing, running, and analyzing simulations to study the physical movements of atoms and molecules in various environments. These postdocs often collaborate with experimental scientists and use high-performance computing to investigate topics in chemistry, biology, materials science, or physics. The position is usually temporary and intended to provide advanced training and research experience after earning a doctorate.

What are the typical interdisciplinary collaborations involved in a Molecular Dynamics Simulations Postdoc position?

A Molecular Dynamics Simulations Postdoc often works closely with experimentalists, chemists, biologists, and computational scientists to interpret simulation results and guide experimental design. This collaboration ensures that computational findings are validated and can meaningfully contribute to broader scientific questions. Postdocs may participate in regular interdisciplinary meetings, contribute to joint publications, and help translate complex simulation data for non-specialist team members. Such collaborations not only enhance the quality of research but also expand professional networks and open avenues for future career opportunities.
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Molecular Dynamics Simulations Postdoc jobs near you
Infographic showing various Molecular Dynamics Simulations Postdoc job openings in the United States as of June 2026, with employment types broken down into 3% Internship, 92% Full Time, and 5% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $65,880 per year, or $31.7 per hour.

Research Scientist I/II, Statistical Mechanics and Dynamics

Lila Sciences

Cambridge, MA

Other

Posted 20 days ago

Job description

Your Impact at LILA

Your role in our Physical Sciences division will focus on designing and implementing state-of-the-art simulation approaches to model transport, kinetics, rare events, and reaction networks, and integrating them with AI-driven platforms for materials discovery. Your work will be integral to our efforts in predicting, designing, and controlling the behavior of complex materials and molecular systems, and their acceleration via agentic AI. You will partner with diverse teams at Lila, including machine learning experts working on scientific superintelligence and materials science experts performing real-world experiments.

What You'll Be Building

  • Develop and extend molecular dynamics and Monte Carlo algorithms to capture rare events, non-equilibrium processes, transport phenomena, and mapping complex reaction networks.
  • Build scalable simulation workflows that integrate statistical mechanics methods with machine learned interatomic potentials and agentic AI frameworks.
  • Design methods for coupling dynamics simulations with experimental observables to enable closed-loop verification and discovery with automated labs.
  • Collaborate with computational scientists, machine learning experts, and platform engineers to advance the fidelity and scalability of simulation-driven materials discovery.
  • Establish reproducible, modular software pipelines for statistical mechanics and dynamics simulations that can be deployed on HPC and cloud-based infrastructure.

What You'll Need to Succeed

  • PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields.
  • Strong background in statistical mechanics, free energy calculations, reaction mapping, non-equilibrium dynamics, and rare-event sampling.
  • Demonstrated expertise with molecular dynamics, Monte Carlo, and/or kinetic simulation software and frameworks (LAMMPS, GROMACS, OpenMM, HOOMD, etc.).
  • Solid programming skills and experience with scientific computing (Python, C/C++, MPI, CUDA, etc.).
  • Experience running and automating simulations on HPC and/or cloud environments at scale.

Bonus Points For

  • Strong publication record applying advanced statistical mechanics or dynamics simulations to molecular and materials systems, including but not limited to molecular/biomolecular systems and solid-state materials and interfaces.
  • Prior work in coupling dynamics simulations with data-driven, AI-based, and/or agentic frameworks.
  • Good familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow, etc.)
  • Prior experience working with machine learned interatomic potentials, including model training, fine-tuning, and data generation
  • Worked closely with experimental teams to extract and corroborate experimental observables from dynamics simulations

Apply