15 days ago
Base Salary
$129k - $189k/yr
Responsibilities
- Develop and execute high-throughput computational materials discovery and screening workflows.
- Apply machine learning, machine-learned interatomic potentials, surrogate models, and computational materials methods to accelerate materials discovery.
- Develop Python-based workflows for materials data generation, processing, modeling, analysis, and visualization.
- Evaluate computational predictions and prioritize materials and compositions for experimental investigation.
- Collaborate with experimental researchers on validation strategies and interpretation of synthesis and characterization results.
- Establish feedback loops between computation, machine learning, and experiments to improve predictive capability and materials optimization.
- Assess emerging AI and computational materials technologies for ASML engineering challenges.
- Translate promising materials concepts into solutions relevant to ASML products with multidisciplinary engineering teams.
- Document methods, assumptions, results, and technical recommendations for technical and non-technical stakeholders.
Requirements
- PhD in materials science, chemistry, physics, chemical engineering, computational science, or a related technical discipline.
- Experience applying computational methods to materials or chemical systems.
- Experience developing scientific software or data-analysis workflows using Python.
- Experience with machine learning, data-driven modeling, or AI methods applied to scientific or engineering problems.
- Strong foundation in computational materials science, computational chemistry, or computational physics.
- Strong Python programming and scientific computing skills.
- Working knowledge of modern machine learning frameworks and workflows such as PyTorch or equivalent tools.
- Ability to work with large computational datasets and develop reproducible analysis pipelines.
- Understanding of materials synthesis and characterization and their relationship to computational predictions.
- Ability to assess model accuracy, uncertainty, applicability, and limitations and communicate complex technical concepts across disciplines.
- Preferred experience with density functional theory, atomistic simulation, molecular dynamics, machine-learned interatomic potentials, graph neural networks, foundation models for materials, active learning, Bayesian optimization, or related AI-enabled materials discovery methods.
- Preferred experience developing automated or high-throughput simulation and data pipelines.
- Preferred familiarity with materials databases and atomistic modeling tools such as ASE, pymatgen, VASP, Quantum ESPRESSO, or LAMMPS.
- Preferred experience connecting computational predictions with experimental materials synthesis or characterization.
- Preferred experience working in multidisciplinary R&D environments and translating research methods into engineering applications.
Benefits
- Medical, dental, vision, and basic life insurance are offered to employees and their families.
- Employees may participate in the Company’s 401k plan.
- Employees receive eight hours of vacation leave every month.
- Employees receive 13 paid holidays throughout the calendar year.
- All new ASML jobs have a minimum application deadline of 10 days.
Categories
About ASML
ASML designs and builds extreme ultraviolet (EUV) and deep ultraviolet (DUV) photolithography machines, along with control software and lifecycle services, used by chipmakers to mass-produce microchips. Its revenue comes primarily from system sales and installed-base service contracts. Founded in 1984 and headquartered in Veldhoven, Netherlands, ASML supplies major semiconductor manufacturers, including TSMC, Samsung, and Intel.