PhD in Generative AI and Statistics: Coalescent-inspired diffusion models for discrete data
Deadline: 11.05.2026
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.
Integreat – Norwegian Centre for Knowledge-driven Machine Learning - Integreat is a Centre of Excellence, funded by the Research council of Norway. Integreat has two branches, one in Oslo (University of Oslo, UiO) and one in Tromsø (UiT The Arctic University of Norway). Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world.
Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. This will be done by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technologies, ethics and machine learning, in new and unique ways. Focus of Integreat is to develop ground-breaking methods and theories, and by this solving fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
The PhD candidate will work at the interface of machine learning, statistics, probability, and with applications in statistical genetics, developing new theory, algorithms, and scalable implementations.
Starting date as soon as possible and upon individual agreement.
The fellowship period is three years. A fourth year may be considered and it will involve 25 % of other career-promoting work. Other career-promoting work may consist of teaching, supervision, and/or research assistance. This is dependent upon the qualification of the applicant and the current needs of the department of employment.
Successful PhD candidate will be employed at the Institute of Basic Medical Sciences at University of Oslo with place of work at Integreat - Norwegian Centre for Knowledge-driven Machine Learning at University of Oslo.
Applicants may hold a PhD Research Fellowship at the University of Oslo for one fellowship period only.
About Integreat
Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Integreat has two branches, one in Oslo (University of Oslo, UiO) and one in Tromsø (UiT The Arctic University of Norway). This position is in Oslo.
Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technology, ethics and machine learning, in new and unique ways.
The focus of Integreat is to develop ground-breaking methods and theories, and therefore solving fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
More about the position
Modern diffusion models underpin state-of-the-art generative AI for images and continuous data, yet principled diffusion-based methods for discrete sequences (e.g., DNA, RNA, amino acid, and crystals) remain fundamentally underdeveloped. Existing approaches rely on ad hoc corruption mechanisms that lack theoretical grounding and interpretability.
This PhD project aims to develop a novel, mathematically principled generative modeling framework for discrete sequence data by unifying diffusion-based generative modeling with coalescent theory from population genetics. The central idea is to replace heuristic discrete denoising schemes with coalescent-inspired stochastic processes, leveraging the deep duality between forward allele-frequency diffusion and backward genealogical merging processes.
The PhD candidate will work at the interface of machine learning, statistics, probability, and with applications in statistical genetics, developing new theory, algorithms, and scalable implementations.
The project aims at establishing an entirely new class of diffusion-like models for discrete data, positioning the successful candidate at the forefront of modern generative AI research.
The student will be part of an international research environment with co-supervision across statistics and machine learning, and will be encouraged to publish in top-tier venues both in machine learning and in statistics.
Qualification requirements
Candidates are expected to have solid academic background and demonstrate the ability and motivation to develop into independent researchers, and must have:
A Master’s degree (120 ECTS) or an equivalent qualification in machine learning, statistics, mathematics, computer science, physics, or a closely related quantitative discipline, minimum grade B (ECTS grading scale) or equivalent. The Master’s degree must include a thesis of at least 30 ECTS
Good competence in probability, linear algebra, and statistical modelling
Demonstrated proficiency in programming (e.g., Python, PyTorch/JAX, or similar)
Strong interest in method development
Fluent oral and written communication skills in English
Candidates without a master’s degree have until 01.09.2026 to complete the final exam.
Desired qualifications:
Solid foundation in Bayesian statistics, empirical Bayes methods and advanced probabilistic modelling
Familiarity with stochastic processes (e.g., Markov chains, stochastic differential equations)
Prior exposure with Transformer architectures or large-scale sequence modelling
Prior exposure to the theory or implementation of diffusion models (either continuous or discrete)
Personal skills
Demonstrate analytical ability and intellectual curiosity
Be highly motivated for theory-driven, foundational research
Work independently while collaborating effectively in an interdisciplinary team
Show persistence and creativity when tackling technically challenging problems
Have strong ambitions for an academic or research-oriented career in machine learning or statistics
Employment in the position is based on a comprehensive assessment of all qualification requirements applicable to the position, including personal skills.
Integreat is committed to equity, diversity, inclusion, and belonging, guided by our INTEGREAT principles (Integrity, Non‑discrimination, Tact, Environment, Gratitude, Respect, Empathy, Accountability, Transparency). We embed these values in practice through inclusive hiring and structured evaluations, targeted mentoring and career development, and flexible work arrangements and accommodations to ensure everyone can thrive and contribute.
We particularly welcome applications from women and gender minorities who are underrepresented in Science, Technology, Engineering, and Mathematics, as well as applicants with immigrant backgrounds, disabilities, nonlinear career paths, or career breaks. We will take career interruptions and diverse career trajectories into account during evaluation.
Integreat supports candidates through close academic supervision, peer mentoring, and structured researcher training, with room to grow into the role over time.
We offer
A unique and ambitious research environment with opportunities to develop independent research questions at the forefront of modern science
Access to a strong network of top-level national and international collaborators
A vibrant, international academic environment with an active and supportive research community
Structured career development programmes, including an individual professional development plan throughout the PhD period
Mentoring and support structures tailored to early-career researchers
Research mobility funds supporting short research stays and international collaboration
A friendly, inclusive, and collaborative working environment that values diverse perspectives and interdisciplinary exchange
A family-friendly and flexible working environment. We recognise that researchers may have caregiving responsibilities and different life situations, and we actively support work-life balance throughout the PhD period. This includes flexibility in working hours, understanding during periods of increased family responsibility, and facilitation related to conference participation, research stays, and travel, taking caregiving needs into account where feasible
Family-friendly surroundings in Oslo and Tromsø, with rich opportunities for culture, nature, and outdoor activities
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 reliable and generous pension agreement, along with public benefits
Comprehensive welfare schemes supporting both personal and professional well-being
Full access to public health services through membership in the National Insurance Scheme
A clear institutional commitment to gender equality and diversity, with dedicated initiatives and networks for women in science
Cover letter with a statement of motivation and research interests
Curriculum Vitae, summarising education, positions, and academic work
Copies of educational certificates (academic transcripts only)
(If applicable) Documentation of English proficiency
Complete list of publications and academic works
List of 2-3 references (name, relation to candidate, e-mail, and phone number)
Please name your documents using the following format:Document type - Surname - First name
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's 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.
