TWO BATTERY MATERIAL MODELING EXPERTS WITH EXPERTISE IN AI

We have multiple openings to conduct research on multiscale modeling of complex interfaces for energy storage and conversion. This includes simulations of chemistry and transport at solid-liquid and solid-solid interfaces for solid-state batteries. You will work transversely with different business units in a multidisciplinary and international environment.

Essential Duties

• Perform multi-scale simulations of solid-solid and solid-liquid interfaces.
• Perform analysis of pathways and barriers for interfacial chemical reactions and ion transport phenomena.
• Develop structure-composition-property relationships for optimizing transport and reactivity using statistical, analytical, and machine learning methodologies.
• Collaborate with computational and experimental scientists in a multidisciplinary team environment to accomplish research goals.
• Perform other duties as assigned.

Qualifications

• Recent Ph.D. in Materials Science, Chemistry, Physics, or a related field. Masters with critical experience will also be considered.
• Understanding of the concepts of density functional theory and related quantum mechanical methods, and in the application of density functional theory to simulations of chemical reactions and/or ion transport in materials.
• Experience in at least one or many of the following methods: classical or coarse-grained molecular dynamics, potential fitting, phase-field simulations, or finite-element simulations.
• Proficient verbal and written communication skills in English.
• Ability to travel as necessary to interact with customers.

Eligibility/ requirements

• Experience in one or more of the following application areas: solid-state batteries, Li-ion batteries and fuel cells.
• Experience with the development of novel methods for modeling the effects of chemical composition on interfacial electrochemistry, or performing large-scale ab initio simulations on high-performance computing environments, and Proficiency in python, comsol and matlab.
• Experience with the application of statistical, analytical, or machine learning methods for optimizing ion transport at interfaces and in solids.

If you meet the above requirements and are interested in this position, please provide by email (contact@abeegroup.com) a detailed resume and a short personal statement explaining your scientific and research interests.