Research Engineer - Computational Materials Science & Self-Driving Materials Discovery

Job description

Job description Bosch Corporate Research is seeking a Research Engineer to conduct applied research in computational materials science at the atomistic level and contribute to the development of next-generation Bosch products. The Research Engineer will be expected to utilize the full suite of tools available for atomistic simulation, from conventional classical and electronic structure calculations to emerging AI techniques, to rapidly move from conceptualization to qualitative screening to quantitative modeling, in pace with engineering development cycles and experimental campaigns. As connection to physical product development is paramount in this role, the successful candidate will be able and interested in engaging with experimental efforts, both in conventional and self-driving lab contexts, and expanding broad professional networks both within Bosch and externally with industry partners, national labs and academia. Your responsibilities Conduct applied research in materials science rooted in rigorous physical and chemical principles, focusing on simulations at the atomistic level Apply both conventional atomistic methods and emerging AI tools to derive actionable insights on materials behavior, selection, and optimization, supported by appropriate model validation Identify, connect, and work with external collaborators Communicate research results through internal presentations, technical reports, peer-reviewed publications, conference presentations, and intellectual-property disclosures. Contribute to a collaborative, interdisciplinary research environment spanning materials science, physics, chemistry, AI, simulation, software, and experimental automation. Create a scientifically rigorous AI-based framework that empowers ordinary Bosch engineers to confidently apply atomistic simulation to complex, industrially relevant materials with high quality and impact. Required PhD in materials science, chemistry, physics, chemical engineering, mechanical engineering, biophysics or a closely related field. Demonstrated experience in atomistic simulation and computational materials science, including hands-on application in one or more of the following areas: density functional theory, molecular dynamics, Monte Carlo simulation, phase-field modeling, multiscale modeling, as well as high-performance computing environments. Proficiency in programming and software engineering, with demonstrated experience in the scientific Python ecosystem or equivalent computational tools. Documented experience applying AI/machine learning techniques to scientific problems, such as active learning, Bayesian optimization, reinforcement learning, automated workflows, or autonomous lab systems. Demonstrated ability to conduct independent research, including formulating research questions, analyzing complex data, and developing working prototypes or proof-of-concept solutions. Evidence of research impact through peer-reviewed publications, patents, open-source software contributions, or equivalent professional accomplishments. Demonstrated written and verbal communication skills, with the ability to communicate technical concepts across technical disciplines and organizational levels. Preferred Experience with the validation and integration of atomistic simulation results with experimental data, including the use of scale-bridging techniques, identification of appropriate experimental methods, and interaction with experimental teams Broad scientific network to identify collaboration opportunities with state-of-the-art methods and top researchers in academia, national laboratories, and industry Experience working with industry-academic partnerships or multidisciplinary research consortia.