This role pays less than
94%
of similar roles. Most pay
$193,000–$251,862
— the shaded band above.
At the midpoint, this role pays about
$150k
versus about
$222k
for comparable roles.
Based on 240 similar postings.
Employer
About Bosch
Bosch is a global engineering and technology company supplying mobility solutions, industrial technology, consumer goods, and energy and building technology; Robert Bosch LLC is its North American arm.
Bosch currently has
34 open roles
on FindRole.
Listed pay typically runs
$165,000–$185,000
across 16 roles with salary data.
The Research Engineer joins the Computational Materials Science team to conduct applied research in materials science rooted in rigorous physical and chemical principles. The role focuses on atomistic level simulations to derive actionable insights on materials behavior, selection, and optimization for products involving sensors, electrochemistry, energy conversion, and sustainability. You will utilize density functional theory, molecular dynamics, Monte Carlo simulation, phase-field modeling, and multiscale modeling within high-performance computing environments. Key responsibilities include applying AI techniques like active learning, Bayesian optimization, and reinforcement learning to create a scientifically rigorous framework for engineers. You will engage with experimental efforts, including self-driving labs, and communicate results through technical reports and peer-reviewed publications. Required skills include a PhD, proficiency in the scientific Python ecosystem, and experience with automated workflows.
Conduct applied research in materials science focusing on simulations at the atomistic level.
Apply conventional atomistic methods and AI tools to derive insights on materials behavior and optimization.
Validate models against experimental data from conventional and self-driving lab contexts.
Create a scientifically rigorous AI-based framework for engineers to apply atomistic simulations to industrial materials.
Communicate research results through technical reports, peer-reviewed publications, and intellectual-property disclosures.
Identify and engage with external collaborators in industry, national labs, and academia.
Develop working prototypes and proof-of-concept solutions for complex scientific problems.
What we're looking for
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 density functional theory, molecular dynamics, Monte Carlo, or multiscale modeling.
Proficiency in programming and software engineering with experience in the scientific Python ecosystem or equivalent tools.
Documented experience applying AI/machine learning techniques to scientific problems like active learning, Bayesian optimization, or reinforcement learning.
Demonstrated ability to conduct independent research, formulate research questions, and develop 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 to convey technical concepts across different disciplines and organizational levels.
Experience with validation of atomistic simulation results with experimental data and scale-bridging techniques (preferred).
C++
Python
High-Performance Computing
GPU Programming
Monte Carlo
Finite Element Method
Numerical Optimization
Machine Learning
Digital Twin
Parallel Algorithms
Computational Geometry
Level-set Methods
Ray Tracing
Multiphysics Modeling
Scientific Computing
Artificial Intelligence
Machine Learning
Computer Vision
Image Processing
Python
Inpainting
Image Generation
Image Restoration
low level vision
Physically Based Rendering
3D Scene Data
Scene Relighting
Illumination Transfer
Segmentation
Statistics