Computational Materials Scientist

Computational Materials ScientistbrWe’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.brThis role is critical to our ability to generate high-fidelity scientific data, validate predictive models, and bridge computational insights with experimental outcomes.brKey Responsibilities:brAdvanced Simulation Development Scientific ComputingbrDesign, 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.brArchitect and optimize computational pipelines capable of generating and managing large-scale materials datasets comprising tens of thousands of compounds, structures, and simulation outputs.brDevelop novel simulation strategies and workflow automation tools to improve throughput, reproducibility, and scientific rigor.brScientific Data Generation ValidationbrGenerate high-quality computational datasets for AI/ML model training, validation, and benchmarking across diverse materials systems.brEstablish rigorous validation frameworks to benchmark simulation outputs against experimental measurements and published scientific literature.brEvaluate uncertainty, accuracy, and predictive performance of computational methodologies across multiple materials domains.brCross-Functional Research LeadershipbrPartner closely with experimental scientists, materials engineers, and machine learning researchers to align computational predictions with real-world material behavior.brTranslate experimental observations into simulation hypotheses and computational models that accelerate research and product development.brTranslate experimental and physical insights into data-driven and machine learning-based models for materials discovery and optimization.brProvide scientific leadership on computational methodologies, simulation best practices, and data quality standards across research programs.brInnovation Technical ExcellencebrDrive continuous improvements in data quality, coverage, reproducibility, and scalability of scientific workflows.brContribute to the development of next-generation computational frameworks that integrate physics-based simulation with AI-driven materials discovery.brStay at the forefront of advances in computational materials science, high-performance computing, and scientific machine learning.brQualifications:brPhD 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).brStrong academic background from a top-tier university in core materials science and physics, including quantum mechanics, thermodynamics, and solid-state physics.brExtensive 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.brDemonstrated 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 approachesbrProven track record of validating computational predictions against experimental data and translating simulation results into actionable scientific insights.brProven 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.brDemonstrated combination of deep materials science expertise with formal academic training or graduate-level coursework in machine learning, computer science, or related quantitative fields.brExperience working with large-scale scientific datasets and computational workflows.brStrong experience working in interdisciplinary environments involving experimental researchers, computational scientists, and machine learning teams.brProficiency with scientific computing, programming skills ( required), workflow orchestration, high-performance computing environments, and large-scale data analysis.brExcellent written and verbal communication skills in English.brPreferred:brExposure to state of the art machine learning, including reinforcement learning or large language modelsbrWhy Join Us:brWork alongside world-class researchers and engineers tackling frontier challenges in materials discovery and scientific AI.brLead mission-critical computational research that directly influences breakthrough technologies and products.brAccess cutting-edge computational infrastructure and collaborative multidisciplinary research environments.brCompetitive compensation, comprehensive benefits, and flexible working arrangements.brOpportunity to make a visible and lasting impact on the future of materials innovation jid10348a9a jit0832a jiy26a