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Postdoc Dft Jobs (NOW HIRING)

Ucf

Postdoctoral Scholar, Physics

Orlando, FL · On-site

Shyam Kattel at the University of Central Florida, has an immediate opening for a postdoctoral ... Expertise with DFT, GC-DFT, and machine learning methods. * Strong oral and written communication ...

Ucf

Post Doctoral Scholar, Physics

Orlando, FL · On-site

The Postdoctoral Associate's Responsibilities include but are not limited to: * Performing computational modeling using DFT, GC-DFT, MD (AIMD, classical force fields, MLIP force fields) * Developing ...

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Postdoc Dft information

What is a postdoc DFT?

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 are the key skills and qualifications needed to thrive as a postdoc in Density Functional Theory (DFT)?

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 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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What job categories do people searching Postdoc Dft jobs look for? The top searched job categories for Postdoc Dft jobs are:
Postdoc Dft jobs near you
Infographic showing various Postdoc Dft job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, and 6% Temporary. Highlights an 100% In-person job distribution.

Postdoctoral Research Associate - Isayev Lab

Carnegie Mellon University

Pittsburgh, PA • On-site

Full-time

Re-posted 13 days ago

Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

67th of 617 rated colleges and universities

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.
Equal Employment Opportunity Statement
Carnegie Mellon University is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.

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