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What is an HPC postdoc?

HPC Postdocs, or High Performance Computing Postdoctoral Researchers, are scientists who have completed their PhD and are conducting advanced research that leverages powerful computational systems to solve complex problems. They often work at universities, research institutes, or national laboratories, focusing on developing new algorithms, optimizing code for supercomputers, or applying computational methods to scientific challenges. HPC Postdocs collaborate with interdisciplinary teams and may also provide support or training in HPC techniques for other researchers. Their work is essential for pushing the boundaries of science in fields such as physics, climate modeling, computational biology, and engineering.

What skills and qualifications are needed to thrive as an HPC postdoc?

To thrive as an HPC Postdoc, you need advanced knowledge in computational science, parallel programming, and a relevant doctoral degree, often in fields like physics, computer science, or engineering. Expertise in programming languages (such as C/C++, Fortran, Python), experience with high-performance computing systems, and familiarity with tools like MPI, OpenMP, or GPU computing frameworks are typically required. Strong analytical thinking, problem-solving, and collaboration skills set outstanding candidates apart in research environments. These competencies are crucial for conducting cutting-edge research, optimizing complex simulations, and advancing scientific discovery using supercomputing resources.

What are common challenges faced by an HPC postdoc when optimizing scientific applications for supercomputing environments?

HPC Postdocs often encounter the challenge of adapting complex scientific codes to efficiently utilize advanced supercomputing architectures. This may involve addressing issues such as parallel scalability, memory bottlenecks, and compatibility with different hardware or software environments. Collaborating closely with domain scientists, software engineers, and IT staff is essential to troubleshoot performance issues and implement effective solutions. Staying up-to-date with emerging HPC technologies and optimization techniques is also crucial for success in this role.
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Infographic showing various Hpc Postdoc job openings in the United States as of August 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Postdoctoral Research Associate - Isayev Lab

Carnegie Mellon University

Pittsburgh, PA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

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8.6

Company rating: 8.6 out of 10

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Job description

Description
The Isayev Lab at Carnegie Mellon University invites applications for a postdoctoral researcher to lead projects at the interface of computational chemistry, machine learning, reaction mechanism elucidation, and automated molecular discovery. The position is ideal for a candidate who wants to turn deep mechanistic understanding into predictive models and closed-loop discovery workflows.
Our lab develops and applies machine learning methods for computational chemistry, materials science, and molecular discovery, including transferable neural network potentials, generative molecular design, and experiment-automation workflows. The postdoc will work in a collaborative CMU environment spanning computational chemistry, AI, automated experimentation, polymer chemistry, and catalysis.
Research directions may include:
Developing automated DFT / ML workflows for mechanistic studies of photoredox, organometallic, and radical catalytic reactions.
Building predictive models that connect quantum-chemical descriptors, catalyst structure, substrate scope, selectivity, and reaction performance.
Applying AIMNet2 and related ML/QM methods to accelerate conformer search, reaction-path exploration, catalyst screening, and high-throughput mechanistic modeling.
Designing closed-loop computational-experimental campaigns for transition metal catalysis, polymer synthesis, and related catalytic transformations.
Creating reusable, open, well-documented software workflows for reaction data generation, curation, featurization, and model deployment.
Collaborating with experimental groups at CMU and external partners to convert mechanistic hypotheses into experimentally testable predictions.
Qualifications
Desired background:
Ph.D. in chemistry, chemical engineering, materials science, or a related field.
Strong experience in computational reaction mechanisms, especially DFT studies of organic, organometallic, photoredox, radical, or homogeneous catalytic systems.
Fluency with Python and modern scientific computing workflows; experience with Git, HPC clusters, SLURM, Gaussian, ORCA, Q-Chem, xTB, RDKit, ASE, or related tools is highly valued.
Interest in machine learning, statistical modeling, active learning, descriptor development, or data-driven reaction prediction.
Ability to work closely with experimental collaborators and communicate mechanistic insight clearly.
Application Instructions
Applications, including a cover letter and a curriculum vitae indicating your interest and relevant training should be submitted electronically via Interfolio.

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