Research Engineer Computational Materials Science SelfDriving Materials Discovery

Bosch Group; Watertown (MA); On-site; USD 140,000 - 160,000; Full time

Job summary

Bosch Corporate Research is seeking a Research Engineer to conduct applied research in atomistic materials science and to develop AI-enabled simulation frameworks. You will collaborate with internal teams and external partners to move concepts from screening to quantitative modeling within Bosch engineering cycles.

The role emphasizes engaging with experimental efforts and contributing to a network across industry, national labs, and academia, with a strong publication and IP track record.

Qualifications

• PhD required in materials science, chemistry, physics, chemical engineering, mechanical engineering, biophysics or related field.
• Hands-on atomistic simulation experience (DFT, MD, Monte Carlo, phase-field, multiscale) and HPC exposure.
• Proficiency in Python and scientific computing tools.
• Experience applying AI/ML to scientific problems (active learning, Bayesian optimization, reinforcement learning, automated workflows).
• Demonstrated ability to conduct independent research and develop working prototypes or proofs of concept.
• Evidence of research impact through publications, patents, or open-source contributions.
• Strong written and verbal communication across technical disciplines.

Responsibilities

• Conduct applied research in materials science rooted in physical and chemical principles, focusing on atomistic simulations.
• Apply conventional atomistic methods and AI tools to derive actionable insights and validate models.
• Identify, connect with external collaborators.
• Communicate research results through internal presentations, reports, publications, and IP disclosures.
• Contribute to interdisciplinary environment spanning materials science, physics, chemistry, AI, and experimental automation.
• Create an AI-based framework enabling Bosch engineers to apply atomistic simulation effectively.

Skills

Atomistic simulation

Python programming

AI/ML in science

Independent research

Scientific communication

Education

PhD (materials science/chemistry/physics/engineering)

Tools

Python ecosystem

HPC environments

Job description

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 modelling, multiscale modelling, 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 optimisation, 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 organisational 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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