PhD and/or 5+ years of experience in computational chemistry, materials science, chemical physics, or a closely related field, with a focus in soft matter physics / polymers - You will lead the computational chemistry track: DFT screening, machine-learned interatomic potentials (MLIPs), and the bridge between quantum chemistry and our wet-lab dataset, while the polymer synthesis lab comes online in parallel. Your role is to ensure the computational layer (DFT features, MLIP physics, SCFT, active learning signals) is ready when the experimental data starts flowing. * Build the computational data pipeline from DFT features to training-ready representations * Experience connecting computational predictions to experimental validation. * Background in concept bottleneck models, physics-informed ML, or interpretable ML for materials * Contributions to open-source computational chemistry or ML tooling
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