PhD: Computational rational design of electrocatalysts for sustainable nitrogen conversion and

OverviewThe candidate will join the Atomistic & Molecular Modelling for Catalysis Group at CIC energiGUNE. This dynamic, multidisciplinary team focuses on computational modelling of catalytic reactions and the development of high-throughput workflows and AI-driven approaches for accelerated catalyst discovery. The PhD researcher will contribute to the computational modelling and rational design of materials for sustainable nitrogen conversion and utilization, with a particular focus on NOx electroreduction, NH3 electrooxidation, and related processes.Main Job FunctionsModelling catalyst resting states and reaction mechanisms under experimentally relevant conditions, providing atomistic insights into the factors governing key electrocatalytic processes.Developing high-throughput computational screening workflows to identify and evaluate novel materials with enhanced catalytic performance.Designing and applying machine learning models to accelerate the discovery of promising electrocatalysts and guide experimental validation.Collaborating closely with experimental partners within CIC energiGUNE and beyond, ensuring effective integration of theoretical and experimental efforts.Presenting research findings in group meetings, project meetings, and at national and international scientific conferences.GroupAtomistic & Molecular Modelling for Catalysis Research GroupWhat We OfferWe are offering a 3-year predoctoral contract and advantageous professional development opportunities with the possibility of 1 year extension based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs.Full access to cutting-edge laboratory facilities and characterization platforms.The incorporation to a top research center in Europe that makes high quality research and impactful contributions to the energy and sustainability fields.Professional and personal development: opportunity to attend seminars, international conferences, trainings, etc.Integrated, enthusiastic, international and multidisciplinary environment.Life-balance BenefitsFlexible working hours promoting work-life balance and self-organization.On-site work model with the option to telework.A welcome program that offers help with finding accommodation and addresses other aspects to help you integrate into the local environment (such as free language courses, assistance with the administrative procedures, help with schools for children).For more information: ApplyAll applicants are invited to submit their application with a detailed curriculum vitae along with a motivation letter and the bachelor degree records at this website.The selection process ends once the candidate is selected.CIC energiGUNE is committed to affirmative action, equal opportunity, and the diversity of its workforce. Therefore, as part of our commitment to inclusion, we welcome applications regardless of gender identity, ethnicity, sexual orientation, disability and age.Description Of The InstitutionWHO ARE WE? ARE WE? FACILITIES: more details on CIC energiGUNE's research activities please visit our website at degree (or equivalent) in Chemistry, Physics, Computational Chemistry, Nanoscience, Chemical Engineering, or a related field.Background in modelling (electro)catalytic processes using density functional theory (DFT), or a keen interest and readiness to develop expertise in this areaExperience in programming (e.g. Python)Excellent oral and written communication skills in English, essential for working in an international and collaborative environment.A collaborative and supportive attitude, with the ability to work effectively within a interdisciplinary team and to actively engage with both internal colleagues and external project partners.We Will Highly ValuePrevious experience with quantum chemistry software packages, such as VASP, Quantum Espresso, or GaussianExperience in high-performance computing (HPC) facilities and knowledge of microkinetic modelling techniquesKnowledge in high-throughput computational methods, machine learning approaches, or workflow automation in computational catalysisNote: This description preserves the original content while improving structure and readability. No tables or presentational fonts are used. All emphasis has been removed except for the intended bold headings.#J-18808-Ljbffr