The University of Stavanger is offering a PhD Fellowship in molecular modelling and machine learning for improved subsurface utilization, based at the Department of Mathematics and Physics within the Faculty of Science and Technology. This position is affiliated with the National Centre for Sustainable Subsurface Utilization of the Norwegian Continental Shelf (NCS2030), a research centre funded by the Norwegian Research Council, industry partners, and academic institutions. The successful candidate will join a vibrant academic community and participate in a three-year, full-time doctoral program focused exclusively on research duties. The research project aims to understand how modified brines ('Smart Water') affect liquid–liquid and liquid–solid interfacial behaviour in low-permeable porous media, with a particular focus on wettability and capillary forces. These phenomena are influenced by mineral surface chemistry, brine composition, and the distribution of ions and impurities at interfaces. Advanced atomic-scale modelling and machine learning approaches will be used to gain predictive insights, with case studies drawn from tight petroleum reservoirs on the Norwegian continental shelf. The project encourages the development and testing of new strategies that account for low permeability and the complex interplay of rock-fluid properties and chemistry. Applicants are required to submit a project proposal (maximum two pages) outlining their chosen topic, problem statement, relevance, theoretical and methodological approach. The proposal will be further developed during the first three months of employment in collaboration with supervisors. The position offers access to career guidance, participation in national and international research environments, and opportunities for academic communication, including a trial lecture and public defence. Eligibility requires a five-year master's degree (or equivalent) in molecular modelling, statistical physics, or machine learning, preferably completed recently. Additional competence in computational physics/chemistry, geosciences, reservoir engineering, or modelling is preferred. Both the master's thesis grade and the weighted average grade must be equivalent to or better than a B on the Norwegian scale. International applicants must provide documentation of English proficiency (TOEFL IBT ≥90, IELTS ≥6.5, CAE/CPE, or PTE Academic ≥62), unless exempted by specific criteria. Motivation, research potential, teamwork, creativity, and the ability to handle a heavy workload are also considered. The position offers a competitive salary (NOK 541,800 gross per year), automatic membership in the Norwegian Public Service Pension Fund, favourable insurance and retirement benefits, gym and sports club membership, discounted public transport, optimal health services, generous parental leave, guaranteed nursery places, and a relocation programme. Diversity and inclusion are valued, and the university encourages applications from all backgrounds, including those with disabilities, gaps in their CV, or immigrant backgrounds. Female applicants are given priority when qualifications are equal. Applications must be submitted via the Jobbnorge portal, including an application letter, project proposal, CV, references, certificates/diplomas, Diploma Supplement/conversion scale (if needed), documentation of English proficiency (if needed), and relevant publications. All documentation should be in English or a Scandinavian language and compressed if over 30 MB. The hiring process includes assessment by an internal expert committee, interviews, and reference checks. The appointee will be based at the University of Stavanger, with the possibility of a research stay abroad. For further information, contact Professor Enrico Riccardi ([email protected]), Head of Department Bjørn H. Auestad ([email protected]), HR advisor Rosa Andrade ([email protected]), or Professor Alejandro Escalona Varela ([email protected]). More details and application links are available at Jobbnorge and Euraxess .