Sandboxaq Senior Research Scientist, Magnets at SandboxAQ to drive computational discovery and optimization of next-generation magnet materials. Apply simulations and machine learning to predict and screen magnet properties.
Responsibilities
Introduction to the team: The Magnets team is one of several research verticals within SandboxAQ's broader Chemical Simulation group, which uses advanced computational methods, physics-based simulation, and AI to discover and develop novel materials. Within that effort, the Magnets vertical is focused on one of the most strategically important materials problems today: developing high-performance permanent magnets that reduce or eliminate reliance on neodymium and other rare earth elements and can be manufactured in the United States. Our goal is to create novel magnet formulations that use existing industry processes and equipment while matching or exceeding the performance of today's leading NdFeB magnets in demanding applications such as semiconductor equipment, vacuum systems, precision motion devices, and defense technologies.
Introduction to the role: We are seeking a Research Scientist to help drive the computational discovery and optimization of next-generation magnet materials. The ideal candidate has a strong background in applying first-principles and multi-scale reference systems, computational simulation, together with machine learning, to magnetic and intermetallic materials. In this role you will (1) run and interpret atomistic and multi-scale simulations that predict the properties of magnets materials, (2) build and deploy computational and ML-driven workflows that screen rare-earth-lean and rare-earth-free candidates at scale, (3) work with experimental collaborators to test and refine predictions against real-world data, and (4) contribute to the project plans, publications, and IP of the vertical. You will report to the Chief Scientist, Magnets and work closely with our AI simulation platform and data-generation teams.
Execute, analyze, and document DFT and multi-scale reference systems to predict phase stability, magnetocrystalline anisotropy, saturation magnetization, Curie temperature, and other properties across Fe- and Co-based intermetallic systems using U.S.-accessible alloying elements.
Develop and test computational workflows and ML interatomic potentials / surrogate models that enable high-throughput screening and optimization of rare-earth-lean and rare-earth-free permanent magnet candidates.
Lead high-fidelity data generation campaigns that feed the magnetism-aware LQM; work with platform and data-generation teams to specify the energetics, descriptors, and uncertainty estimates the workflows require.
Connect atomistic simulation outputs to microstructure- and process-level predictions (powder metallurgy, sintering, heat treatment) to keep candidate formulations manufacturable with existing industry equipment.
Collaborate with experimental validation partners to test and iterate on model predictions, contributing to the Design-Build-Test-Learn (DBTL) feedback loop between computation and the lab, ensuring that LQM models are more accurate over time.
Work within defined projects, deliverables and milestones alongside agile, multidisciplinary teams, and generate and evaluate hypotheses that influence project direction.
Communicate research findings through scientific talks, peer-reviewed publications, patents, and partner-facing technical presentations; support junior scientists and interns as the team grows.
Essential Skills & Experience
PhD or equivalent experience in Solid- state Physics, Quantum Chemistry applied to Materials Science or a related field.
Hands-on experience with DFT or atomistic simulation.
