Johns Hopkins University
Postdoctoral Fellow - Atomistic Simulations and AI for Materials Design
Johns Hopkins University, Baltimore, Maryland, United States, 21276
Overview
The AtomGPTLab, led by Dr. Kamal Choudhary at Johns Hopkins University, invites applications for a Postdoctoral Fellow position in 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 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. The AtomGPTLab envisions a platform that integrates rigorous quantum-mechanical methods with scalable ML methods to accelerate materials discovery and design.
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 molecular dynamics (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. Collaborate with interdisciplinary teams and engage with experimental groups, national labs, and industry partners.
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
Develop and apply high-throughput workflows and pipelines for materials discovery. Lead development of AI-assisted materials design and synthesis planning. Coordinate with experimental collaborators and contribute to grant proposals.
Application Instructions
Applicants should submit a curriculum vitae and three recent publications. Review of applications will begin in mid-August 2025. To apply for this position, visit: apply.interfolio.com/171117 Salary and Benefits
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 geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University. Total Rewards Johns Hopkins offers a total rewards package that supports employees\' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/. Equal Opportunity and Additional Information
Equal Opportunity Employer The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. 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 characteristic. 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 that are irrelevant to the program involved. Pre-Employment Information If you require special assistance or accommodation during the pre-employment process, please contact the HR Business Services Office. For TTY users, call via Maryland Relay or dial 711. The successful candidate(s) for this position will be subject to a pre-employment background check including education verification. EEO is the Law: https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf Diversity and Inclusion The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion. The university encourages, but does not require, vaccination as described in policy language. For details, please visit the linked pages.
#J-18808-Ljbffr
The AtomGPTLab, led by Dr. Kamal Choudhary at Johns Hopkins University, invites applications for a Postdoctoral Fellow position in 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 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. The AtomGPTLab envisions a platform that integrates rigorous quantum-mechanical methods with scalable ML methods to accelerate materials discovery and design.
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 molecular dynamics (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. Collaborate with interdisciplinary teams and engage with experimental groups, national labs, and industry partners.
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
Develop and apply high-throughput workflows and pipelines for materials discovery. Lead development of AI-assisted materials design and synthesis planning. Coordinate with experimental collaborators and contribute to grant proposals.
Application Instructions
Applicants should submit a curriculum vitae and three recent publications. Review of applications will begin in mid-August 2025. To apply for this position, visit: apply.interfolio.com/171117 Salary and Benefits
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 geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University. Total Rewards Johns Hopkins offers a total rewards package that supports employees\' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/. Equal Opportunity and Additional Information
Equal Opportunity Employer The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. 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 characteristic. 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 that are irrelevant to the program involved. Pre-Employment Information If you require special assistance or accommodation during the pre-employment process, please contact the HR Business Services Office. For TTY users, call via Maryland Relay or dial 711. The successful candidate(s) for this position will be subject to a pre-employment background check including education verification. EEO is the Law: https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf Diversity and Inclusion The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion. The university encourages, but does not require, vaccination as described in policy language. For details, please visit the linked pages.
#J-18808-Ljbffr