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🚀 Mission Highlights As a Computational Chemist specialised into electronic & functional materials , at Entalpic, you will work at the intersection of quantum chemistry, electronic structure theory, and Machine Learning (ML), aiming to accelerate the discovery of next-generation thin-film and interface materials for high-performance industrial applications. Your role centres on the computational characterisation of electronic, dielectric, optical, and transport properties of materials and interfaces — building (beyond) DFT x ML workflows — across a portfolio of projects spanning advanced semiconductor components, photonic materials, and atomic layer processes. You will be a key contributor to Entalpic's core discovery pipeline, owning the electronic structure modelling capabilities that bridge atomic-scale quantum chemistry with device-relevant property targets; for the semiconductor industry. ✨ 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: Electronic structure & property modelling — Lead DFT and beyond-DFT investigations of electronic, dielectric, and optical properties of thin-film candidates and interfaces: band gaps, work functions, dielectric constants, refractive indices, carrier effective masses, and interfacial band alignment across a range of material families (oxides, nitrides, metals, 2D materials). Experience in Density Functional Perturbation Theory, phonon-electron coupling phenomena, and modeling magnetic properties. Surface & interface modelling — Experience in modeling solid-solid heterostructures/interfaces and electronic transport at the interface, including interfacial band alignment, interfacial charge transfer and interfacial electronic transport across metal/dielectric and channel/gate stacks. High-throughput DFT workflows & agentification — Design, run, and automate large-scale quantum mechanical simulations using tools such as VASP or CP2K, within workflow managers (Jobflow, Atomate2, Custodian), contributing to systematic property datasets across broad material spaces. Drive the automation and orchestration of DFT pipelines, reducing human-in-the-loop bottlenecks. ML model application & fine-tuning — Apply and fine-tune MLIPs and electronic property predictors on DFT-generated datasets; contribute to model validation against experimental and high-fidelity reference data. Scientific leadership — Contribute to publications, patents, and client-facing deliverables; engage with industrial and academic partners on technical results; mentor junior team members and interns. 🤓 Expertise & Skills PhD in Computational Chemistry, Condensed Matter Physics, Materials Science, or a closely related field, with 2+ years of industry experience . Deep expertise in electronic structure methods — extensive hands-on experience with DFT packages (VASP, CP2K, or equivalent), including hybrid functionals (HSE06), GW approximation, or DFPT for excited-state and optical properties. Strong background in functional property prediction — band structure, density of states, dielectric tensors, effective masses, work functions, optical spectra, and interface band alignment. Proven track record in high-throughput DFT workflows. Experience with ML models applied to materials — knowledge of MLIPs (eg MACE, UMA); experience with fine-tuning or active learning workflows is a strong asset. Experience in bridging scale from atomistic simulation to device properties in semiconductors Proficiency in Python, PyTorch, Slurm, and version control (Git). Strong analytical skills and ability to drive projects independently in a fast-paced startup environment. Excellent communication skills in English; ability to present results to both scientific and non-technical audiences. Bonus Skills: Expe