Postdoctoral Researcher in Computational Materials Science
- Johns Hopkins. University
- Location: Baltimore, MD
- Job Number: 7161664
- Posting Date: 3 months ago
- Salary / Pay Rate: NRSA Standard
- Application Deadline: Open Until Filled
Job Description
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 innovating clean hydrogen production, waste-heat conversion, nuclear power generation, and energy storage.
Qualifications
A. Doctorate in materials science, physics, chemistry, computer science or related fields.
B. Excellent communication skills in English, both written and verbal. The dissemination of research findings through peer-reviewed articles and presentations is mission critical.
C. Ability to lead research projects and collaborate with experimentalists.
D. Understanding of thermodynamics of materials, solid-state physics, inorganic chemistry, and metallurgy.
E. Strong programming skills in C++ and Python and proficiency with Unix systems.
F. Proven experience with VASP, Quantum ESPRESSO, LAMMPS, or other ab-initio codes.
G. Expertise in any of the following areas: high-entropy materials, disorder, phonons, magnetism, catalysis, machine learning/artificial intelligence, database/API development, aflow.org repositories.
Graduate students near the completion of their Ph.D. are welcome to apply.
Qualifications
A. Doctorate in materials science, physics, chemistry, computer science or related fields.
B. Excellent communication skills in English, both written and verbal. The dissemination of research findings through peer-reviewed articles and presentations is mission critical.
C. Ability to lead research projects and collaborate with experimentalists.
D. Understanding of thermodynamics of materials, solid-state physics, inorganic chemistry, and metallurgy.
E. Strong programming skills in C++ and Python and proficiency with Unix systems.
F. Proven experience with VASP, Quantum ESPRESSO, LAMMPS, or other ab-initio codes.
G. Expertise in any of the following areas: high-entropy materials, disorder, phonons, magnetism, catalysis, machine learning/artificial intelligence, database/API development, aflow.org repositories.
Graduate students near the completion of their Ph.D. are welcome to apply.