Job Information
Johns Hopkins University Postdoctoral Researcher ( PREP0002399) in Gaithersburg, Maryland
PREP Research Associate
CHIPS Funded Project.
This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires that such institutions be the recipients of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research Title:
CHIPS Postdoc for Density Functional Theory Workflow
The work will entail:
using density functional theory to predict electronic properties of semiconductor devices. Some of the key properties include bandgaps, band alignments and transport properties of interfaces, especially for metal-semiconductor, insulator-semiconductor heterostructures. Some of the materials of interest for interface design include Si, GaN, AlN, Al2O3, HfO2, Al, Cu.
Key responsibilities will include but are not limited to:
? Development of a streamlined workflow to conduct high-throughput calculations for semiconductor-specific properties, including transport properties.
? Development of a Python package to generate industry-relevant atomistic structures for material interfaces and heterostructures
? Simulate coherent and semi-coherent interfaces, reconstruction effects, amorphous materials, as well as the incorporation of point and line defects within the interfaces,
? Calculate properties such as bandgaps, band offsets, Schottky barriers, current-voltage curves, mobility, and thermal conductivity. This will involve the utilization of software tools such as VASP, Quantum Espresso, QuantumATK, or similar platforms
? Benchmarking calculations with respect to available experimental data,
? Design and implementation of surrogate models for the generated data to expedite the interface design process using specialized software such as ALIGNN.
? A Doctorate in Materials Science and Engineering, Physics or a related field.
? 4 years of relevant experience.
? Experience with using VASP, Abinit or QuantumATK to perform atomistic simulations.
? Experience with using Scikit-Learnt to perform machine learning surrogate model development.
? Familiarity with Python.
? Strong oral and written communication skills.
Please upload the following (preferably in a single PDF) with your application:
Cover Letter
CV/Resume
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Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) (https://www.nist.gov/iaao/academic-affairs-office/nist-professional-research-experience-program-prep) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate the administrative functions of the PREP Program.
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Job Type: Full Time Johns Hopkins University is committed to active recruitment of a diverse faculty and student body. The University is an Affirmative Action/Equal Opportunity Employer of women, minorities, protected veterans and individuals with disabilities and encourages applications from these and other protected group members. Consistent with the University’s goals of achieving excellence in all areas, we will assess the comprehensive qualifications of each applicant.
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