3-years PhD position in probabilistic machine learning and statistics
Deadline: 04.10.2026
Publisert
The University of Oslo is Norway’s oldest and highest ranked educational and research institution, with 26 500 students and 7 200 employees. With its broad range of academic disciplines and internationally recognised research communities, UiO is an important contributor to society.
The Institute of Basic Medical Sciences overall objective is to promote basic medical knowledge in order to understand normal processes, provide insight into mechanisms that cause illness, and promote good health. The Institute is responsible for teaching in basic medical sciences for the programmes of professional study in medicine and the Master's programme in clinical nutrition. The Institute has more than 300 employees and is located in Domus Medica.
About the position
We invite applications for a three-year PhD Research Fellowship in probabilistic machine learning and statistics at the Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway.
The preferred starting date is as soon as possible and will be agreed upon with the successful candidate.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
Place of work is the Department of Biostatistics (OCBE), Domus Medica, Gaustad UiO campus, Oslo.
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.
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.
What skills are important in this role?
Required Qualification
The Faculty of Medicine has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this and expected to be in the upper segment of their class with respect to academic credentials.
Master’s degree or equivalent in statistics, machine learning, mathematics, computer science, physics, or a closely related quantitative discipline. In the case of a foreign completed degree (M.Sc.-level), this must correspond to a minimum of four years in the Norwegian educational system.
The Master’s project should address methodological questions using mathematical, statistical, or computational tools. The candidate must demonstrate a strong interest in methodological development.
Proven competence in probability, linear algebra, and statistical modelling is essential for this position.
A strong interest in probabilistic modelling, latent-variable methods or Bayesian methodology is essential.
Demonstrated strong proficiency in programming (e.g., Python, PyTorch/JAX, or similar) and computational skills are required for this position.
Candidates must have excellent interpersonal and communication skills. Personal suitability and an interest in the themes connected to the funded project will be emphasized.
Working language in the group and at OCBE is English, hence excellent communication skills in written and oral English are a prerequisite. English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8
Grade requirements The norm is as follows:
The average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational system
The average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system
The Master’s thesis must have the grade B or better in the Norwegian educational system
Candidates without a master’s degree have until December 31st to complete the final exam.
The purpose of the fellowship is research training leading to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/
All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation, and the national security regulation.
Desired qualifications
Background in Bayesian computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage.
Experience with computational methods for large or high-dimensional datasets is an advantage.
Experience with biomedical applications is an advantage.
Experience with interdisciplinary collaboration is an advantage.
Personal skills
The successful candidate is expected to:
Demonstrate analytical ability and intellectual curiosity
Be highly motivated for theory-driven, foundational research
Show persistence and creativity when tackling technically challenging problems
Work independently while collaborating effectively in an interdisciplinary team
Have strong ambitions for an academic or research-oriented career in machine learning, statistics or computational biology
Show ability to work in a structured manner and swiftly adapting to new tasks
Show good communication and collaboration skills
Show a positive attitude towards and the ability to handle hectic periods
Employment in the position is based on a comprehensive assessment of all qualification requirements applicable to the position, including skills.
Colourbox
We need different perspectives in our work
UiO is an open and internationally oriented comprehensive university that strives to be an inclusive and diverse workplace and academic environment. You can read more about UiO’s work on equality, inclusion, and diversity at uio.no.
We fulfill our mission most effectively when we draw upon our variety of experiences, backgrounds, and perspectives. We are looking for great colleagues—could you be the next one?
We will provide appropriate accommodations should you need them. Relevant adjustments may include modifications to working hours, task adaptations, digital, technical, or physical adjustments, or other practical measures.
If you have an immigrant background, a disability, or CV gaps, we encourage you to indicate this in the job application portal. We always invite at least one qualified candidate from each group for an interview. In this context, disability is defined as an applicant who identifies as having a disability that requires workplace or employment-related accommodations. For more details about the requirements, please refer to the Employer portal (Norwegian).
The selections made in the job application portal are used for anonymized statistics that all state employers include in their annual reports.
More information about gender equality initiatives at UiO can be found here.
We hope you will apply for the position with us.
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.
General information
The best qualified candidates will invited for interviews.
Applicant lists can be published in accordance with Norwegian Freedom of Information Act § 25. When you apply for a position with us, your name will appear on the public applicant list. It is possible to request to be excluded from this list. You must justify why you want an exemption from publication and we will then decide whether we can grant your request. If we cannot, you will hear from us.
