Postdoctoral Researcher - Materials Informatics
PhD-level Academic / Postdoc roles on Crosslinked list a median of $100k (225 that show pay). This posting does not list pay.
Lead computational and data-driven discovery of fusion, fission and redox energy materials using ab-initio methods and machine learning.
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Worth knowing
Why it is interesting. Combines ab-initio simulation, machine learning and quantum computing for fusion and fission materials discovery.
Might not be for you. Purely computational and inorganic/metallic focus, with no polymer or experimental bench work.
Adjacent materials rolePostdoc in data-driven discovery of energy materials: fusion/fission materials, high-entropy materials, ab-initio codes (VASP, LAMMPS), thermodynamics of materials, metallurgy
- Seniority
- Postdoc
- Work
- On-site
- Type
- Postdoc
- Degree
- PhD
- Industry
- Energy
- Role
- Academic / Postdoc
Role details
The Entropy for Energy ( S4E ) Laboratory at Johns Hopkins University (PI Corey Oses) has openings for postdoctoral researchers in the data-driven discovery of energy materials. Projects focus on fusion and fission materials, redox chemistry, and quantum-computing-enabled materials discovery. A. Doctorate in materials science, physics, chemistry, computer science, quantum information science, or related fields. B. Ability to move to and work at Johns Hopkins University, USA. C. Proven track-record of first-author publications and presentations. A minimum of three peer-reviewed, first-author publications must be accessible by the S4E team. D.
Proven experience with VASP, Quantum ESPRESSO, LAMMPS, or other ab-initio codes. E. Strong programming skills in C++ and Python and proficiency with Unix systems. F. Ability to lead research projects and collaborate with experimentalists. G. Fundamental understanding of thermodynamics of materials, solid-state physics, inorganic chemistry, and metallurgy at the level of theory/implementation. H. Expertise in any of the following areas: high-entropy materials, disorder, phonons, magnetism, catalysis, machine learning/artificial intelligence, database/API development, quantum computing for materials science. Graduate students near the completion of their Ph.D. are welcome to apply.
Please provide your anticipated defense date and expected graduation date under Qualification B.
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