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What are the key skills and qualifications needed to thrive as a Postdoc in Density Functional Theory (DFT), and why are they important?

To thrive as a Postdoc specializing in Density Functional Theory (DFT), you need a PhD in physics, chemistry, or materials science with a strong background in quantum mechanics and computational modeling. Proficiency in quantum chemistry software packages (such as VASP, Quantum ESPRESSO, or Gaussian), programming languages (like Python or Fortran), and experience with HPC systems is typically required. Strong analytical thinking, effective scientific communication, and the ability to work independently and collaboratively are standout soft skills. These competencies are crucial for conducting advanced research, publishing impactful results, and contributing to interdisciplinary scientific teams.

What are some common challenges faced by Postdoc DFT researchers when working on collaborative projects?

Postdoc DFT researchers often collaborate with experimentalists, theorists, and computational scientists, which requires clear communication and effective coordination. One common challenge is translating computational results into experimentally meaningful predictions, given the differences in language and expectations between disciplines. Additionally, managing computational resources and ensuring reproducibility of results can be demanding, especially when working with large datasets or complex systems. Working in interdisciplinary teams, postdocs must balance their independent research goals with group objectives and deadlines, fostering adaptability and strong teamwork skills.

What are Postdoc DFT positions?

Postdoc DFT positions are postdoctoral research roles focused on Density Functional Theory (DFT), a computational quantum mechanical modeling method used in physics, chemistry, and materials science. These positions typically involve conducting advanced research using DFT to study the electronic structure of atoms, molecules, or solids. Postdocs in this area often work on developing new DFT methods, applying them to novel materials, or interpreting experimental results. The role requires a strong background in computational modeling, quantum mechanics, and often programming skills.

What is the difference between Postdoc Dft vs Postdoctoral Research Associate?

AspectPostdoc DftPostdoctoral Research Associate
Required CredentialsPhD in relevant fieldPhD in relevant field
Work EnvironmentAcademic labs, research institutionsAcademic labs, research institutions
Employer & Industry UsageUniversities, research centersUniversities, research centers
Common Search & ComparisonYesYes

Both Postdoc Dft and Postdoctoral Research Associate roles typically require a PhD and involve research in academic or research institutions. The main difference lies in terminology; 'Postdoc Dft' is often used in specific regions or institutions, while 'Postdoctoral Research Associate' is more common in others. Both positions focus on advanced research, with similar work environments and employer types.

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Postdoc Dft jobs near you
Infographic showing various Postdoc Dft job openings in the United States as of May 2026, with employment types broken down into 83% Full Time, and 17% Part Time. Highlights an 100% In-person job distribution.
Postdoctoral Fellow - Atomistic Simulations and AI for Materials Design

Postdoctoral Fellow - Atomistic Simulations and AI for Materials Design

Johns Hopkins University

Baltimore, MD • On-site

$48.70K - $66.10K/yr

Full-time

Posted 12 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 200 frontline employees who took The Breakroom Quiz

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Job description

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.

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