Computational Chemist

Do you want to contribute to a more sustainable world? Are you excited to deploy computational chemistry and materials science to help shape the future of battery technology and consequently the future of consumer electronics and mobility?

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  Design, build, and validate atomistic simulation frameworks (density functional theory DFT, molecular dynamics MD, and related methods) for liquid electrolytes and their interfaces with silicon anodes and lithium-ion cathodes.

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  Quantify bulk electrolyte properties such as solvation structure, ion pairing, transport behavior, and thermodynamic stability under relevant operating conditions.

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  Investigate interfacial reactions and decomposition pathways related to SEI formation, oxidative stability, and high-temperature degradation.

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  Derive physically interpretable descriptors from simulation outputs and structure them for integration into internal machine learning pipelines.

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  Collaborate closely with machine learning scientists, battery scientists and data team to translate battery performance questions into focused computational studies.

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  Develop clean, reproducible Python code for simulation analysis, data processing, and workflow automation.

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  Build and maintain scalable, automated simulation workflows to enable efficient and reproducible studies.

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  Support force field parameterization, validation, and benchmarking where required.

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  Operate effectively with complex, evolving, and sometimes incomplete experimental data.

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  Use version control (e.g., Git) for collaborative development of scripts, analysis tools, and workflows.

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  Clearly communicate modeling assumptions, limitations, uncertainty, and conclusions to multidisciplinary stakeholders.