Materialwissenschaftler/in (Computational Materials Scientist) für Simulation und KI-gestützte Materialforschung

We’re hiring a Computational Materials Scientist with a strong background in both physics-based simulation and machine learning-driven scientific modeling to build and scale domain-specific simulation and data generation workflows. You’ll work with ML researchers and experimental teams to ensure high-quality data for model training and evaluation.

This role is critical to our ability to generate high-fidelity scientific data, validate predictive models, and bridge computational insights with experimental outcomes.

Key Responsibilities

Advanced Simulation Development & Scientific Computing

• Design, develop, and scale high-throughput computational materials workflows utilizing Density Functional Theory (DFT), Molecular Dynamics (MD), phase-field modeling, and related first-principles simulation methods, including their application to solid-state synthesis processes and phase transformations.
• Architect and optimize computational pipelines capable of generating and managing large-scale materials datasets comprising tens of thousands of compounds, structures, and simulation outputs.
• Develop novel simulation strategies and workflow automation tools to improve throughput, reproducibility, and scientific rigor.

Scientific Data Generation & Validation

• Generate high-quality computational datasets for AI/ML model training, validation, and benchmarking across diverse materials systems.
• Establish rigorous validation frameworks to benchmark simulation outputs against experimental measurements and published scientific literature.
• Evaluate uncertainty, accuracy, and predictive performance of computational methodologies across multiple materials domains.

Cross-Functional Research Leadership

• Partner closely with experimental scientists, materials engineers, and machine learning researchers to align computational predictions with real-world material behavior.
• Translate experimental observations into simulation hypotheses and computational models that accelerate research and product development.
• Translate experimental and physical insights into data-driven and machine learning-based models for materials discovery and optimization.
• Provide scientific leadership on computational methodologies, simulation best practices, and data quality standards across research programs.

Innovation & Technical Excellence

• Drive continuous improvements in data quality, coverage, reproducibility, and scalability of scientific workflows.
• Contribute to the development of next-generation computational frameworks that integrate physics-based simulation with AI-driven materials discovery.
• Stay at the forefront of advances in computational materials science, high-performance computing, and scientific machine learning.

Qualifications

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computational Science, or a closely related quantitative discipline (candidates near completion of the PhD may also be considered).
• Strong academic background from a top-tier university in core materials science and physics, including quantum mechanics, thermodynamics, and solid-state physics.
• Extensive experience developing and deploying advanced computational materials science workflows using DFT, MD, or equivalent atomistic and mesoscale simulation techniques, including applications to solid-state synthesis, thermodynamic analysis, and phase transformations.
• Demonstrated expertise in high-throughput simulation of large materials libraries, including datasets containing 10,000+ materials, structures, or computational experiments combined with machine-learning-based force fields or related hybrid modeling approaches
• Proven track record of validating computational predictions against experimental data and translating simulation results into actionable scientific insights.
• Proven ability to integrate physics-based modeling with data-driven or machine learning approaches, including experience in synthetic data generation or advanced AI methods applied to scientific workflows.
• Demonstrated combination of deep materials science expertise with formal academic training or graduate-level coursework in machine learning, computer science, or related quantitative fields.
• Experience working with large-scale scientific datasets and computational workflows.
• Strong experience working in interdisciplinary environments involving experimental researchers, computational scientists, and machine learning teams.
• Proficiency with scientific computing, programming skills (Python required), workflow orchestration, high-performance computing environments, and large-scale data analysis.
• Excellent written and verbal communication skills in English.

Preferred

• Exposure to state of the art machine learning, including reinforcement learning or large language models

Why Join Us

• Work alongside world-class researchers and engineers tackling frontier challenges in materials discovery and scientific AI.
• Lead mission-critical computational research that directly influences breakthrough technologies and products.
• Access cutting-edge computational infrastructure and collaborative multidisciplinary research environments.
• Competitive compensation, comprehensive benefits, and flexible working arrangements.
• Opportunity to make a visible and lasting impact on the future of materials innovation