Job Opportunity: Computational Materials Scientist
We are seeking a talented Computational Materials Scientist with a robust background in physics-based simulation and machine learning-driven scientific modeling. In this role, you will develop and scale domain-specific simulation and data generation workflows, collaborating closely with ML researchers and experimental teams to ensure the delivery of high-quality data for model training and evaluation.
This position is vital for our capacity to produce high-fidelity scientific data, validate predictive models, and connect 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 applied to solid-state synthesis processes and phase transformations.
- Architect and optimize computational pipelines capable of generating and managing extensive materials datasets comprising tens of thousands of compounds, structures, and simulation outputs.
- Develop innovative simulation strategies and workflow automation tools to enhance 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:
- Collaborate 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.
- Convert 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 nearing 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 in developing and deploying advanced computational materials science workflows using DFT, MD, or equivalent atomistic and mesoscale simulation techniques.
- Demonstrated expertise in high-throughput simulation of large materials libraries, including datasets containing over 10,000 materials and structures.
- Proven track record of validating computational predictions against experimental data and translating simulation results into actionable scientific insights.
- Ability to integrate physics-based modeling with data-driven or machine learning approaches, including experience in synthetic data generation.
- Combination of deep materials science expertise with formal academic training in machine learning, computer science, or related quantitative fields.
- Familiarity with large-scale scientific datasets and computational workflows.
- Experience in interdisciplinary environments, collaborating with experimental researchers, computational scientists, and machine learning teams.
- Proficiency in 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 Qualifications:
- Experience with state-of-the-art machine learning techniques, including reinforcement learning or large language models.
Why Join Us:
- Collaborate with world-class researchers and engineers tackling pioneering challenges in materials discovery and scientific AI.
- Lead mission-critical computational research that influences breakthrough technologies and products.
- Access cutting-edge computational infrastructure and participate in collaborative multidisciplinary research environments.
- Enjoy competitive compensation, comprehensive benefits, and flexible working arrangements.
- Seize the opportunity to make a visible and lasting impact on the future of materials innovation.
Apply online using the form below. Please note that only applications matching the job profile will be considered.