Computational Materials Scientist for Machine-Learned Interatomic Potentials (MD/DFT)

The role: We are developing a new class of material: an ultrathin, vapour-deposited hybrid materials with custom properties - and we want to understand the fundamental properties. You will own our first atomistic simulation campaign: predicting conductivity (electron, ion, heat), hydration behaviour, and interfacial transport in a material that has never been simulated, and testing your predictions against experiments that we'll run constantly alongside. The workflow (DFT benchmarking → amorphous structure generation → fine-tuned machine-learned interatomic potentials with active learning → large-scale transport MD, enhanced sampling, and path-integral MD) is scoped, budgeted, and precedented in the recent literature; the material is not. You will be our first dedicated computational hire, working directly with the experimental team.

What you'll do: Benchmark DFT functionals against coupled-cluster references for proton-transfer energetics, and own the resulting error budget Build amorphous model structures of a hybrid metal-organic network validated against our data Fine-tune foundation MLIPs (MACE-class) with active learning; run DFT labelling campaigns Run nanosecond-scale transport MD, metadynamics, and PIMD to extract diffusion coefficients, activation energies, isotope effects, and interfacial behaviour Turn simulation into decisions: feed results into our materials and process development, and publish the methodology with us Define our simulation infrastructure (environments, data management, reproducibility)

What we're looking for: PhD in computational chemistry, physics, or materials science Hands-on ab-initio MD experience on HPCYou have trained at least one machine-learned interatomic potential (MACE, NequIP, Allegro, DeepMD, GAP, or similar) and can explain how you built the training set and knew it was converged Strong scientific Python

Nice to have: Ion transport background Amorphous/disordered structure generation Path-integral MD (i-PI), GCMC (RASPA), charge-partitioning workflows, or workflow engines (psiflow, Parsl, Snakemake)A benchmark culture: DLPNO-CCSD(T), functional selection, error bars you can defend