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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:

Atomistic and multiscale machine learning for predictive semiconductor heterointerface design

The work will entail:

The new NIST program Multiscale Modeling and Validation of Semiconductor Materials and Devices is seeking applicants for a postdoctoral researcher role in the area of atomistic and multiscale machine learning for predictive semiconductor heterointerface design.

The researcher will collaborate with a diverse team performing high throughput classical molecular dynamics simulation, Tight Binding / Density Functional Theory / Quantum Monte Carlo calculations, and multi-physics device modeling to build atomistic and coarse-grained material and device property models that will guide a data-driven computational workflow to generate valuable reference and training data.

The main goal of this role is to integrate multi-physics and multi-length scales modeling approaches of varying fidelity and computational cost to accurately model semiconductor technologies, from the atomistic level to the device level. The ultimate technical goal is rapid quantitative prediction of useful industrial quantities (i.e., current-voltage response and transconductance field effect transistor characteristics) for realistic devices with specific interfaces and defect type/density.

We are seeking postdoctoral applicants interested in joining the team responsible for developing and deploying the machine learning components of this modeling capability.

Key responsibilities will include but are not limited to:

• Develop and apply novel machine learning methods for accelerated (beyond molecular dynamics) prediction of equilibrium structure and defect energetics of heterointerface structures, with particular focus on semiconductor technologies (Si, GaN)

• Develop and apply multiscale machine learning methods for predictive modeling and inverse design of semiconductor device properties, specifically by linking surrogate models at the material property (e.g., electronic band gap, thermal conductivity, carrier mobilities) level and the continuum device level (e.g., surrogate models for semiconductor technology computer aided design (TCAD)).

• Collaborate with team members to train and evaluate scalable and chemically transferrable machine learning force fields to support the above multiscale modeling responsibilities

  § Collaborate with team members to develop, integrate, and validate robust and fast uncertainty quantification methods to enable active learning, out of distribution detection, and evaluation of systematic model bias

  § Collaborate with modeling team members to develop reference, validation, and training datasets, and to deploy machine learning models at scale for high throughput screening and optimal allocation of theory-based modeling

§ Present results at internal meetings, and occasional meetings with external stakeholders.

§ Present research results at technical conferences and in peer reviewed scientific journal articles.

§ Perform computational experiments in a reproducible and well documented manner.

§ Ensure that results, protocols, software, and documentation have been archived or otherwise transmitted to the larger organization.

§ PhD in Materials Science, Computer Science, Engineering, or a related field.

§ Strong oral and written communication skills substantiated by multiple clearly-written lead author publications and conference presentations in the field of applied machine learning for materials simulation and characterization.

§ Demonstrated experience developing, validating, and applying machine learning methods and models for material simulation.

§ Demonstrated experience with uncertainty quantification and dataset bias evaluation and mitigation for machine learning systems.

§ Experience with atomistic modeling of multiphase materials and interface systems, including firsthand experience with density functional theory calculations, molecular dynamics, and advanced materials modeling techniques such as metadynamics.

§ Ability to work with atomistic simulation data at scale.

§ Strong experience in multiple programming languages (Python, C/C , Julia, etc) and familiarity with parallel computing technologies (such as MPI and CUDA).

§ Strong scientific computing skills, including source code control and workflow management in high performance computing environments.

Please upload the following (preferably in a single PDF) with your application:

• Cover Letter

• CV/Resume

Privacy Act Statement

Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)

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.

Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.

Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.

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.