Research Scientist, Quantum Chemistry

Research Scientist, Quantum Chemistry ​ Location: Cambridge, MA or London, UK Start Date: Immediate Position Type: Full-time ​ About the Role ​ We're seeking a computational chemist with exceptional quantum chemistry expertise to drive breakthrough discoveries in catalytic kinetic mechanism elucidation. This role seeks a candidate who is passionate about mechanistic research, iterates quickly through computational hypotheses, and isn't afraid to challenge established kinetic paradigms. You'll build quantum chemical workflows that generate high-quality simulated kinetic data and comparative studies across diverse catalytic systems (homogeneous, heterogenous, or enzymatic), working in a highly collaborative environment where your mechanistic insights and computational protocols directly support AI/ML model development. ​ What You'll Do ​ Design and execute quantum chemical calculations to study kinetics and mechanisms in homogeneous catalysis using state-of-the-art DFT and post-HF methods Develop systematic computational studies to predict and analyze kinetic parameters across varying catalytic systems and reaction conditions Build and validate robust, reproducible computational protocols for kinetic data generation, benchmarking methodologies, and statistical analysis frameworks Design comprehensive datasets of simulated kinetic data to support AI/ML model training and validation efforts Work closely with AI/ML researchers to establish data quality standards, feature engineering approaches, and validation protocols for machine learning applications Take ownership of complex kinetic mechanism problems and deliver high-quality computational datasets under tight timelines Pioneer novel quantum chemical approaches for systematic kinetic analysis that push the boundaries of mechanism discovery across diverse catalytic systems ​ What We're Looking For ​ Essential Experience: ​ PhD in computational chemistry, theoretical chemistry, or related field with demonstrated expertise in catalysis research Strong publication record with first-author papers in high-impact journals focusing on catalytic mechanism studies Deep hands-on experience with quantum chemistry packages for catalysis applications (e.g., Gaussian, ORCA, etc.) Proven expertise in DFT method selection, basis set optimization, and dispersion corrections for transition metal catalysis, Demonstrated track record of kinetic mechanism studies through computational approaches (transition state location, IRC analysis, thermodynamic/kinetic analysis, rate constant calculations) Proficiency in Python and experience with computational chemistry libraries (ASE, cclib, RDKit, OpenMM) and data analysis frameworks Experience with comparative kinetic studies, reaction coordinate analysis, free energy calculations, and statistical analysis of kinetic data ​ Highly Preferred: ​ First-author publications specifically in computational kinetics and homogeneous catalysis mechanism studies Experience with heterogeneous catalysis (surface chemistry, adsorption studies, periodic DFT calculations) and enzyme catalysis Knowledge of advanced quantum chemical methods (CASPT2, DLPNO-CCSD(T), multi-reference approaches) for challenging catalytic systems Experience with high-throughput computational workflows and systematic parameter studies Background in statistical analysis, uncertainty quantification, and computational protocol benchmarking Experience with data curation, dataset design, and collaboration with machine learning teams Background in high-performance computing and automated workflow development ​ Essential Qualities: ​ Research Excellence: You have a proven track record of independent research with high-impact publications and novel mechanistic insights Data-Driven Approach: You design systematic computational studies with rigorous statistical analysis and uncertainty quantification of kinetic parameters Protocol Development: You build robust, benchmarked computational workflows that generate reliable, reproducible kinetic data for diverse applications Cross-Functional Collaboration: You work effectively with AI/ML teams, understanding data requirements and quality standards for machine learning applications ​ Why This Role is Different ​ This isn't a traditional computational chemistry position focused on catalyst optimization. You'll be working at the intersection of fundamental kinetic mechanism discovery and data science, with direct impact on AI/ML model development. Our team moves quickly from kinetic hypotheses to systematic computational studies, and you'll see your protocols and datasets integrated into machine learning pipelines within weeks. If you're energized by the prospect of your kinetic mechanism studies and computational protocols directly enabling next-generation AI models for catalysis, this role is for you. ​ Apply ​ Send your resume or CV, a brief cover letter highlighting your most impactful kinetic mechanism research and systematic computational studies, a complete publication list with first-author papers clearly marked, and links to any relevant code repositories (e.g., GitHub) to careers@dayhofflabs.com.