Dayhoff Labs

Computational Chemist (Cambridge, MA)

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We are seeking a talented and motivated computational chemist with expertise in scaling up molecular dynamics simulations for proteins, small molecules, and heterogeneous systems using neural network potentials. This position offers an exciting opportunity to work at the forefront of computational chemistry, with a focus on investigating reaction mechanisms and advancing our understanding of chemical reaction networks.

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The ideal candidate will have demonstrated experience in applying or developing neural network potentials to study reaction mechanisms in heterogeneous catalysis or enzymatic catalysis. Candidates should have at least one preprint or peer-reviewed publication showcasing the application of these tools.

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Responsibilities:

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• Perform advanced research using molecular dynamics simulations powered by neural network potentials.

• Scale up simulations for complex systems, including proteins, small molecules, and heterogeneous systems.

• Investigate reaction mechanisms in catalysis, with a particular focus on heterogeneous and enzymatic systems.

• Collaborate with interdisciplinary teams of chemists and engineers to bridge computational insights with experimental data.

• Develop new computational workflows and methodologies for efficient simulation of chemical systems.

• Analyze and interpret large-scale datasets generated from simulations and present findings in clear, actionable formats.

• Contribute to high-impact research articles, technical reports, and presentations.

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Minimum Qualifications:

• Ph.D. in computational chemistry, theoretical chemistry, chemical engineering, or a related field.

• Expertise in molecular dynamics simulations and neural network potentials.

• Proficiency in computational chemistry software and simulation tools (e.g., LAMMPS, AMBER, GROMACS, or equivalent).

• Strong programming skills, particularly in Python, with experience using chemistry-focused libraries (e.g., RDKit).

• At least one preprint or peer-reviewed publication showcasing the use of neural network potentials for molecular dynamics.

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Preferred Qualifications:

• Experience applying or developing neural network potentials to investigate reaction mechanisms in heterogeneous catalysis or enzymatic catalysis.

• Strong interest in chemical reaction networks and their application to understanding complex chemical systems.

• A proven track record of impactful research, demonstrated through publications in high-impact journals and conference presentations.

• Ability to work both independently and collaboratively in a multidisciplinary team environment.

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To Apply:
Submit your resume and a cover letter outlining your experience and suitability for the role to careers@dayhofflabs.com. We look forward to hearing from you!