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?
About LeydenJar
LeydenJar aims to become the world’s innovation leader in advanced battery anodes to energize batteries for human progress. High energy density of battery cells is an urgent and unmet need for a wide range of industries from consumer electronics to electric vehicles. By addressing the bottleneck of the battery anode, LeydenJar can improve this by 50-70% in an industry that can only improve 3% per year. Our pure silicon anode is regarded as the holy grail to achieve this in the battery industry. Progress is not only about enabling new products, it also means a more sustainable supply chain, resilience and access to battery technology in geopolitical context and enabling electrification.
Further advancement of durable performance under a wide range of conditions (high voltage, high/low temperatures, fast charging) requires a fundamentally deeper understanding of electrolyte behavior and interfacial chemistry. We are building internal computational capability to accelerate electrolyte development and reduce experimental iteration cycles. This role of Computational Chemist is central to that effort.
Your mission
As a Computational Chemist at LeydenJar, your mission is to generate mechanistic insight into liquid lithium-ion electrolytes and their interactions with 100% silicon anodes. You will develop and deploy atomistic simulation frameworks that convert complex molecular behavior into physically interpretable descriptors and predictive insights.
Your work will directly accelerate electrolyte development, reduce experimental iteration cycles, and improve high-temperature and high-SOC performance of our next-generation battery technology.
Your primary location will be Leiden. You may occasionally be required to work at our Eindhoven site.
You will join the Battery R&D team under the CTO line and work closely with Battery R&D, Product Development, and Machine Learning/Data Engineering colleagues. Together, you will translate atomistic simulations into structured datasets, predictive features, and actionable guidance for experimental programs.
You will help build LeydenJar’s internal computational capability from the ground up, operating at the intersection of electrochemistry, materials science, and data science. You are expected to deliver rigorous science with startup-level speed and pragmatism.
Your responsibilities:
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.
Quantify bulk electrolyte properties such as solvation structure, ion pairing, transport behavior, and thermodynamic stability under relevant operating conditions.
Investigate interfacial reactions and decomposition pathways related to SEI formation, oxidative stability, and high-temperature degradation.
Derive physically interpretable descriptors from simulation outputs and structure them for integration into internal machine learning pipelines.
Collaborate closely with machine learning scientists, battery scientists and data team to translate battery performance questions into focused computational studies.
Develop clean, reproducible Python code for simulation analysis, data processing, and workflow automation.
Build and maintain scalable, automated simulation workflows to enable efficient and reproducible studies.
Support force field parameterization, validation, and benchmarking where required.
Operate effectively with complex, evolving, and sometimes incomplete experimental data.
Use version control (e.g., Git) for collaborative development of scripts, analysis tools, and workflows.
Clearly communicate modeling assumptions, limitations, uncertainty, and conclusions to multidisciplinary stakeholders.