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

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The Bosch Research and Technology Center North America with offices in Watertown, MA, Sunnyvale, CA, and Pittsburgh, PA is part of the global Bosch Group ( a company with over 90 billion euro revenue, 410,000 people worldwide, a very diverse product portfolio, and a history of over 125 years. The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Human Machine Interaction (HMI), Robotics, Energy Technologies, Internet Technologies, Circuit Design, Semiconductors, Wireless, MEMS Advanced Design, and Healthcare.

The Computational Materials Science team in Watertown specializes in atomistic and mesoscale simulation to improve Bosch products through deep understanding of thermodynamic, kinetic, and transport phenomena on an atomic level. Using quantum mechanical and classical simulations, and machine learning, our team focuses on application areas which include sensors, electrochemistry, energy conversion, and sustainability. Our work directly contributes to the development of innovative solutions for real-world challenges.

Do you want to shape beneficial technologies with your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.

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.

Qualifications

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.

Additional Information

The annual U.S. base salary range for this position is $140,000-$160,000. Within the range, individual pay is determined based on several factors, including, but not limited to, type of degree, work experience and job knowledge, complexity of the role, type of position, job location, etc. Your Hiring Manager can share more details about the specific salary range for this position during the interview process.*

All your information will be kept confidential according to EEO guidelines.

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