Valeria Vitelli

Three positions are open in the group of Prof. Valeria Vitelli at the University of Oslo, Norway, within the Oslo Centre for Biostatistics and Epidemiology (OCBE). The group is seeking two PhD candidates and one postdoctoral researcher to work on cutting-edge research at the intersection of biostatistics, machine learning, probabilistic modelling, and genetics. The PhD projects focus on developing new methods for tensor analysis with complex latent dependencies and generative models for discrete sequence data, leveraging advanced machine learning and Bayesian inference techniques. The postdoctoral position is linked to a recently funded project by the Research Council of Norway, focusing on Bayesian models for unsupervised learning, particularly in probabilistic recommender systems. OCBE offers a vibrant research environment with strong international collaborations and close connections to Integreat - Norwegian Centre for Knowledge-driven Machine Learning and dScience – Centre for Computational and Data Science. The positions are fully funded for three years each. Applicants should have a strong background in statistics, mathematics, computer science, or related fields, with experience in machine learning, probabilistic modeling, or genetics being highly desirable. The application deadline is April 19th, 2026. For more information and to apply, visit the University of Oslo vacancies page.

The Oslo Centre for Biostatistics and Epidemiology (OCBE) at the University of Oslo is offering two PhD positions and one three-year postdoctoral researcher position in the fields of biostatistics, machine learning, and genetics. The PhD projects focus on developing new methods for analyzing tensors with complex latent dependencies using machine learning and Bayesian inference, as well as creating generative models for discrete sequence data by combining diffusion models and coalescent theory. The postdoctoral position is linked to a project funded by the Research Council of Norway, centered on Bayesian models for unsupervised learning, particularly in probabilistic recommender systems and rank-based modeling. OCBE is a vibrant research community with strong international collaborations and close ties to Integreat - Norwegian Centre for Knowledge-driven Machine Learning. The positions offer an excellent opportunity to work at the intersection of statistics, mathematics, computer science, and genetics, contributing to cutting-edge research in probabilistic modelling and machine learning. Applicants for the PhD positions should have a solid background in statistics, mathematics, computer science, or related fields, with experience in machine learning, probabilistic modelling, or genetics being advantageous. The postdoctoral candidate should hold a PhD in a relevant discipline and have expertise in Bayesian modelling, preference learning, or recommender systems. The positions are fully funded, with salary and benefits according to University of Oslo regulations. The application deadline is April 19th, 2026. For more information and to apply, visit the official University of Oslo vacancies page. This is an excellent opportunity for motivated candidates to join a leading research group and contribute to innovative projects at the intersection of biostatistics, machine learning, and genetics.

Three fully funded research positions are available at the Oslo Centre for Biostatistics and Epidemiology, University of Oslo (Norway): two PhD positions and one 3-year postdoc position in statistics, machine learning, and biostatistics. The PhD projects sit at the intersection of machine learning, probabilistic modelling, and genetics. One project aims to develop new methods for analysing tensors with complex latent dependencies using machine learning and efficient Bayesian inference (for example, variational approximation). The second PhD project focuses on new generative models for discrete sequence data, combining diffusion models and coalescent theory. The postdoc position is linked to a project funded by the Research Council of Norway and concerns Bayesian models for unsupervised learning when multiple data sources are available, with an emphasis on probabilistic recommender systems. The positions are connected to Integreat, the Norwegian Centre for Knowledge-driven ML, and the broader research group on statistical models for high-dimensional and functional data. The post highlights a strong research environment for early-career researchers. Funding is described as fully funded, with competitive Norwegian salaries typically around 2,500–3,000€ net per month. The post also notes free healthcare and a high quality of life in Oslo. Application deadlines mentioned in the post are 19 April for PhD position 1 and the postdoc, and end of April for PhD position 2 (with the link to be posted later). Interested applicants should use the Jobbnorge links provided, monitor the post for the second PhD application link, and contact the listed supervisors for questions.

The University of Oslo, through Integreat – Norwegian Centre for Knowledge-driven Machine Learning, invites applications for a PhD position in Generative AI and Statistics, focusing on coalescent-inspired diffusion models for discrete data. This doctoral research fellowship is based at the Institute of Basic Medical Sciences and offers a unique opportunity to work at the intersection of machine learning, statistics, probability, and statistical genetics. The successful candidate will develop new theory, algorithms, and scalable implementations, contributing to the advancement of generative modeling for discrete sequence data. Integreat is a Centre of Excellence funded by the Research Council of Norway, with branches in Oslo (University of Oslo) and Tromsø (UiT The Arctic University of Norway). The Oslo branch hosts this position. Integreat’s mission is to develop ground-breaking methods and theories by integrating mathematical and computational cultures from statistics, logic, language technology, ethics, and machine learning. The centre draws on expertise from departments of Mathematics, Informatics, Philosophy, Biostatistics and Epidemiology, the Norwegian Computing Centre, and the ML group at UiT. The PhD project aims to establish 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 innovation is to replace heuristic discrete denoising schemes with coalescent-inspired stochastic processes, leveraging the duality between forward allele-frequency diffusion and backward genealogical merging. The candidate will be positioned at the forefront of generative AI research, with opportunities to publish in top-tier venues in both machine learning and statistics. The research environment is international and interdisciplinary, with co-supervision across statistics and machine learning. The position offers structured career development, mentoring, research mobility funds, and a supportive, inclusive academic community. Integreat is committed to equity, diversity, and inclusion, actively supporting work-life balance and welcoming applications from underrepresented groups in STEM. Funding includes a competitive salary as PhD Research Fellow (NOK 550,800 - 595,000), pension agreement, welfare schemes, and full access to public health services. The fellowship period is three years, with a possible fourth year involving career-promoting work such as teaching or research assistance. Applicants must have a Master’s degree (120 ECTS) or equivalent in machine learning, statistics, mathematics, computer science, physics, or a closely related quantitative discipline, with a minimum grade B (ECTS grading scale). The degree must include a thesis of at least 30 ECTS. Required skills include probability, linear algebra, statistical modeling, programming proficiency (Python, PyTorch/JAX), and fluent English communication. Candidates without a master’s degree have until 01.09.2026 to complete the final exam. Desired qualifications include Bayesian statistics, empirical Bayes, advanced probabilistic modeling, familiarity with stochastic processes, Transformer architectures, and diffusion models. Personal skills such as analytical ability, motivation for foundational research, independence, collaboration, persistence, creativity, and ambition for an academic career are essential. To apply, submit your application via Jobbnorge, including a cover letter, CV, educational certificates, documentation of English proficiency (if applicable), publication list, and 2-3 references. Name documents as 'Document type - Surname - First name'. Foreign applicants should attach an explanation of their university's grading system. All documentation must be in English or a Scandinavian language. The deadline for applications is 11th May 2026. For further information, contact William Denault (Researcher) or Valeria Vitelli (Professor) at the University of Oslo. The University of Oslo is Norway’s oldest and highest ranked educational and research institution, offering a vibrant academic environment and comprehensive support for early-career researchers.

The University of Oslo, Norway’s oldest and highest ranked educational and research institution, invites applications for a PhD position in Generative AI and Statistics, focusing on coalescent-inspired diffusion models for discrete data. The successful candidate will join Integreat – the Norwegian Centre for Knowledge-driven Machine Learning, a Centre of Excellence funded by the Research Council of Norway. Integreat is renowned for its interdisciplinary research, drawing expertise from mathematics, informatics, philosophy, biostatistics, and computer science, and offers a vibrant, international academic environment. This doctoral research fellowship is based at the Institute of Basic Medical Sciences, with the place of work at Integreat in Oslo. The 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 duality between forward allele-frequency diffusion and backward genealogical merging processes. The research will position the candidate at the forefront of modern generative AI, with opportunities to publish in top-tier venues in both machine learning and statistics. The PhD fellowship is for three years, with a possible fourth year involving 25% career-promoting work such as teaching, supervision, or research assistance, depending on qualifications and departmental needs. The position offers a unique and ambitious research environment, access to a strong network of national and international collaborators, structured career development programs, mentoring, research mobility funds, and a family-friendly, flexible working environment. Salary ranges from NOK 550,800 to 595,000 per year, with pension and welfare benefits, and full access to public health services. Applicants must have a Master’s degree (120 ECTS) or equivalent in machine learning, statistics, mathematics, computer science, physics, or a closely related quantitative discipline, with a minimum grade B (ECTS grading scale) or equivalent. The degree must include a thesis of at least 30 ECTS. Required skills include competence in probability, linear algebra, statistical modelling, proficiency in programming (Python, PyTorch/JAX, or similar), and strong interest in method development. Fluent oral and written English is essential. Candidates without a master’s degree must complete the final exam by 01.09.2026. Desired qualifications include Bayesian statistics, empirical Bayes methods, advanced probabilistic modelling, familiarity with stochastic processes, prior exposure to Transformer architectures or large-scale sequence modelling, and experience with diffusion models. Personal skills such as analytical ability, motivation for theory-driven research, independence, collaboration, persistence, creativity, and ambition for an academic or research-oriented career are valued. Integreat is committed to equity, diversity, inclusion, and belonging, with structured evaluations, targeted mentoring, and flexible work arrangements. Applications from women, gender minorities, and candidates with diverse backgrounds are particularly welcomed. To apply, submit your application via Jobbnorge, including a cover letter, CV, educational certificates, documentation of English proficiency (if applicable), publication list, and 2-3 references. Name documents as 'Document type - Surname - First name'. Foreign applicants should attach an explanation of their university's grading system. All documentation must be in English or a Scandinavian language. The deadline for applications is 11th May 2026. For further information, contact William Denault (Researcher) or Valeria Vitelli (Professor) at the provided emails. The best qualified candidates will be invited for interviews. The University of Oslo is committed to gender equality and diversity, with dedicated initiatives and networks for women in science.