About the position
## Job description The project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern biomedical research but remain challenging to analyse because of their scale, heterogeneity, and complex spatial and functional dependencies. Existing methods often rely on restrictive assumptions or application-specific computational workflows. The PhD project aims to address these limitations by developing a unified, scalable, and interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: - Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high-dimensional data. - Develop modular methods that incorporate domain-specific information into latent-variable models. - Investigate methodological questions related to computation, identifiability, uncertainty quantification, and interpretation. - Account for structures arising from spatial relationships, physical constraints, high-dimensional imaging, and clinical covariates. - Apply the resulting methods to spatial transcriptomics and fluorescence imaging data to improve our understanding of complex biological systems. The project is particularly suited to a candidate interested in probabilistic modelling, latent-variable methods, and structured unsupervised learning. Research Environment & Collaboration The successful candidate will work at the interface of probabilistic machine learning, computational statistics, and biostatistics, developing new methodology, inference algorithms, and scalable implementations. By contributing to a new class of structured factor model, the candidate will work on foundational methodological questions motivated by complex biomedical data. - Local Research Infrastructure: You shall take part in the research group on “Statistical models for high-dimensional and functional data”, led by Professor Valeria Vitelli at OCBE, and will actively collaborate on data analyses and research problems with the MolMed Imaging Platform at IMB. - Global Impact: You will join the FunGen-AD consortium, the world’s largest research initiative studying the genetic underpinnings of Alzheimer’s disease. - International Mobility: there are possibilities for arranging a 3 to 6-month research stay at Columbia University in New York (USA). - Publication: Candidates are encouraged to publish in top-tier venues across machine learning (e.g., NeurIPS, ICML), statistics, and computational biology. - Dual Affiliation: The position will be based at and affiliated with the University of Oslo (Norway) and will also be affiliated with Columbia University (USA). The research group on statistical models for high-dimensional and functional data is part of the larger and active research environment on “High-dimensional statistics” at OCBE. OCBE has expanded considerably during the last decade, becoming one of Europe's most active biostatistics groups with currently over 70 researchers. OCBE is internationally recognized, with interests spanning a broad range of research areas - including methods for high-dimensional data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE has numerous collaborations with leading biomedical research groups in Norway and abroad. This PhD position is also embedded in an international research environment with co-supervision across statistics and machine learning. You will be encouraged to collaborate with prestigious US scholars at Columbia University and other affiliated universities within the FunGen-AD consortium. This is a unique opportunity to contribute to cutting-edge research in probabilistic machine learning and statistics by developing innovative methods that combine theoretical, computational, and applied perspectives within a collaborative and supportive academic environment. You will be part of a dynamic group of early career researchers, supervised by two main PIs and senior experts from diverse fields of application. An extension of the appointment by up to twelve months may be considered, which will be devoted to career enhancing compulsory work duties, e.g. teaching or advising. This will be dependent on the qualifications of the applicant and the specific teaching need of the employment department. ## We can offer you - Salary in position as PhD Research Fellow, position code 1017 in salary range NOK 550 800 – 595 000, depending on competence and experience. From the salary, 2% is deducted in statutory contributions to the State Pension Fund. - A friendly professional and stimulating international working environment at OCBE, with committed colleagues that care and help each other. Special focus is also given to newly employed personnel relocating from abroad, with a dedicated central office at UiO for international relocation. - Exciting and meaningful organization with an important societal mission, contributing to knowledge development, education, and enlightenment that promote sustainable, fair, and knowledge-based societal development. - A workplace with good development and career opportunities. Access to a network of top-level national and international collaborators. - Opportunity of up to 1.5 hours a week of exercise during working hours, and employee-dedicated facilities and training classes in the campus. - Good welfare schemes. Read more about the benefits of working in the public sector in Norway at the Employer Portal. - Full access to public health services through membership of the National Insurance Scheme. - A reliable and generous pension agreement via the membership in the State Pension Found, which is one of Norway's best pension schemes with beneficial mortgages and good insurance schemes. - Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities ## How to apply The application must include: - Cover letter - statement of motivation and research interests - CV (summarizing education, positions and academic work - scientific publications) - Transcripts of records, copies of the original Bachelor’s and Master’s degree diploma - Documentation of English proficiency if applicable - List of publications and academic work that the applicant wishes to be considered by the evaluation committee - The master thesis or at least some finished chapter of the master thesis - Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number) Application with attachments must be submitted via our recruitment system Jobbnorge, click "Apply for the position". Foreign applicants should attach an official explanation of their University grading system. When applying for the position, we ask you to retrieve your education results from Vitnemålsportalen.no. If your education results are not available through Vitnemålsportalen, we ask you to upload copies of your transcripts or grades. Please note that all documentation must be in English or a Scandinavian language.
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