Atomistic and Data-Driven Modeling of Materials for Energy Applications - Postdoctoral Researcher

Atomistic and Data-Driven Modeling of Materials for Energy Applications - Postdoctoral Researcher

We have multiple openings for Postdoctoral Researcher Positions to conduct mentored research in atomistic and data-driven modeling of materials for energy applications. Key focus areas include investigating reactivity, transport, and phase evolution at heterogeneous interfaces; predicting materials degradation and coupled chemo-electro-mechanical response under operating conditions; understanding electronic properties of materials under non-equilibrium conditions; and developing data science approaches for predicting materials performance across scales.

You will work closely with a multidisciplinary team in support of projects sponsored by the Basic Energy Sciences, Transportation Technologies Offices, and Office of Electricity within the Department of Energy, and internal LDRD programs. This position is within the Quantum Simulations Group within the Materials for Emerging Applications and Extreme Conditions section of the Materials Science Division in the Physical and Life Sciences Directorate.

This position requires full-time on-site presence due to the nature of the work.

Note:

This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are recent PhDs within five years of the month of the degree award at time of hire date.

You will:

• Perform electronic structure theory-based simulations of complex materials, such as oxides and multi-element systems.
• Perform thermodynamic and kinetic analyses of chemical reactions and phase transitions at surfaces and interfaces.
• Perform non-equilibrium molecular dynamics simulations & first-principles calculations to study materials response under externally applied stimuli.
• Derive structure-composition-property relationships using statistical, analytical, and machine-learning based methodologies.
• Develop machine-learning interatomic potentials and surrogate models based on physics-informed descriptors for property evaluation and prediction.
• Contribute to the planning, design, and execution of assigned research activities under the guidance of senior scientists and project leadership. Collaborate with computational and experimental scientists in a multidisciplinary team environment to accomplish research goals.
• Develop increasing technical independence while pursuing research activities aligned with project and program goals; interact with collaborators within and outside the Laboratory. Document research, contribute to peer-reviewed publications, and present results within the DOE community and at conferences/technical meetings.
• Perform other duties as assigned.

Qualifications:

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
• Ph.D. degree completed, or anticipated to be completed by the hire date, in Materials Science, Chemistry, Physics, Mechanical Engineering or a related field.
• Experience in the application of density functional theory and/or advanced electronic structure theory for simulating complex materials, including oxides and multi-element systems
• Experience performing large-scale molecular dynamics simulations on high-performance computing environments.
• Additional experience in at least one of the following methods: machine-learning based approaches, cluster expansion, advanced statistical analysis, kinetic Monte Carlo simulations, phase-field modeling, reduced order, descriptor or surrogate model development as relevant to energy related applications.
• Ability to perform technical assignments with increasing independence, analyze results, and contribute to solutions for defined research problems. Ability to contribute to research directions under mentorship and communicate results effectively through peer-reviewed publications and technical presentations. Proficient verbal and written communication skills to collaborate effectively in a team environment, prepare written reports and present and explain technical information.
• Interpersonal skills necessary to interact with scientists, engineers and other technical and administrative staff in a collaborative, multidisciplinary team environment.

Qualifications We Desire:

• Experience with computational workflow development and multiscale model integration
• Experience in modeling highly disordered materials and/or heterogeneous interfaces
• Experience with AI/ML-enabled materials discovery, surrogate modeling, and generative models.
• Experience with Bayesian optimization, uncertainty quantification, sensitivity analysis, and Pareto front analysis.
• Experience supporting DOE consortia or multi-laboratory data integration efforts.

Pay Range: $123,048 Annually

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors…