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🚀 Mission Highlights As a Computational Chemist at Entalpic , you will work at the intersection of quantum chemistry, machine learning, and atomic-scale engineering. Your role centres on exploring surface chemistry using DFT and ML methods — investigating reaction pathways, molecular dynamics, and transition states — combined with multi-scale approaches to bridge atomic-scale simulations with process-level behaviour. You will be a key contributor to Entalpic's core discovery pipeline, owning and advancing our atomistic modelling capabilities across real R&D challenges in atomic-scale manufacturing processes (ALD, ALE, CVD) relevant to semiconductors, batteries, photovoltaics, and beyond. ✨ Role & Responsibilities This position directly supports the company's mission of discovering materials and processes to optimize carbon-intensive industries. You will be responsible for: Surface & molecular modelling — Lead investigations into surface reaction mechanisms, adsorption energies, and transition state geometries relevant to atomic layer processes (metal organic complexes), using DFT and semi-empirical methods as primary tools. High-throughput DFT workflows — Design, run, and automate quantum mechanical simulations (surface adsorption, reaction pathways, transition states) using tools such as ASE, VASP, or CP2K, contributing to systematic, large-scale datasets. Multi-scale modelling — Bridge atomic-scale simulations with mesoscale process behaviour using Molecular Dynamics, kinetic Monte Carlo (kMC), meta-dynamics, and QM/MM methods; develop and integrate multi-scale workflows into Entalpic's discovery pipeline. ML model application & fine-tuning — Apply and fine-tune existing ML models (MACE, UMA, etc.) on DFT-generated and experimental data; contribute to model validation and benchmarking against quantum mechanical references. Workflow agentification — Drive the automation and orchestration of DFT pipelines within Entalpic's active learning framework, reducing human-in-the-loop bottlenecks and enabling faster iteration cycles. Scientific leadership — Contribute to publications, patents, and client-facing deliverables ; mentor junior team members and interns; engage with industrial and academic partners to ensure computational discoveries are experimentally grounded. 🤓 Expertise & Skills PhD in Computational Chemistry, Materials Science, Chemical Physics, or a closely related field, with 2+ years of industry experience . Deep expertise in quantum mechanics and DFT — extensive hands-on experience with simulation packages (VASP, CP2K, Orca, LAMMPS, or equivalent) and a strong understanding of the underlying physics. Proven track record in high-throughput computational workflows — experience designing, running, and maintaining large-scale DFT campaigns using workflow managers (e.g. Atomate, Jobflow, Fireworks, ASE workflows). Strong experience with ML models applied to atomistic systems — knowledge of MLIPs or molecular property prediction models; experience with fine-tuning, transfer learning, or active learning workflows is a strong asset. Multi-scale modelling experience — familiarity with at least one of kMC, QM/MM, or related mesoscale methods is a significant plus. Proficiency in Python , PyTorch, Slurm, and version control (Git). Strong analytical skills, scientific rigour, and ability to drive projects independently in a fast-paced startup environment. Excellent communication skills in English; ability to present complex results to both technical and non-technical audiences. Bonus Skills: Experience with surface chemistry modelling, ideally in the context of thin film deposition or atomic layer processes (ALD, ALE, CVD). Prior exposure to organometallic chemistry or precursor design. Familiarity with reaction network generation or automated transition state search tools. Publications in peer-revi