Atomistic Simulations Jobs (NOW HIRING)

Description
The AtomGPTLab, led by Dr. Kamal Choudhary at Johns Hopkins University, invites applications for a Postdoctoral Fellow position in the fields of atomistic simulations, machine-learned force fields, and artificial intelligence (AI). The successful candidate will lead the development of a computational platform that unifies first-principles methods, classical molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials.
The research spans quantum mechanics, statistical physics, and deep learning, and aims to enable AI-guided predictions of synthesizable and functional materials such as superconductors, catalysts, semiconductors, and energy-relevant compounds. The position is embedded in an interdisciplinary and collaborative environment with active interactions across experimental groups and national laboratories.
Qualifications
Basic Qualifications or Specialized Certifications

• A PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computer Science, or a related field.

• Demonstrated experience in one or more of the following: Density Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs).

Extensive Knowledge In:

• First-principles simulations with packages such as VASP, Quantum ESPRESSO, GPAW.

• Machine-learned interatomic potentials (e.g., ALIGNN-FF).

• Structure-property prediction using GNNs (e.g., ALIGNN,).

• LLM fine-tuning and prompt engineering (e.g., HuggingFace, OpenAI, AtomGPT).

Working Knowledge Of:

• Workflow tools (e.g.,JARVIS-Tools, ASE) and HPC environments.

• Software development in Python, Git-based version control, and Conda packaging.

• Data integration and surrogate modeling using experimental and computational datasets.

• Interdisciplinary collaboration and mentoring of students or junior researchers.

Specific Duties & Responsibilities

• Conduct high-throughput DFT calculations and manage large-scale materials datasets.

• Develop GNN architectures for predicting materials properties from atomic graphs.

• Train and deploy machine-learned force fields for MD simulations and rapid screening.

• Fine-tune or pre-train LLMs for generation and analysis of materials structures, synthesis protocols, and characterization outputs.

• Build pipelines for combining experimental and simulated data for inverse design.

• Provide real-time computational feedback to experimental collaborators for synthesis and characterization.

• Lead manuscript writing, conference presentations, and contributions to open-source repositories.

• Mentor undergraduate and graduate students, and participate in grant proposal development.

Additional Opportunities

• Collaborate as Co-PI on interdisciplinary proposals.

• Engage with experimental groups, national labs, and industry partners.

• Participate in the development of open cyberinfrastructure (e.g., AtomGPT.org).

• Attend international conferences and contribute to global research communities.

• Access to cutting-edge computing clusters and experimental characterization tools.

Application Instructions
Applicants should submit a curriculum vitae and three recent publications. Review of applications will begin in mid-August 2025.
Equal Employment Opportunity Statement
Salary Range
The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.
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Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/.
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The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. The University is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved.
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After receiving a conditional offer, the successful candidate(s) for this position will be subject to a pre-employment background check including education verification. When deciding whether a candidate's conviction history is job-disqualifying, the University considers the nature and gravity of the offense, the time that has passed since the conviction, and the nature of the job being sought.
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