Matrix College

Several Postdoctoral researcher and 1-2 doctoral researcher positions in machine learning in Kaski Lab, ELLIS Institute Finland and Manchester Centre for AI Fundamentals Samuel Kaski’s two-part research lab in ELLIS Institute Finland  (Probabilistic Machine Learning, Aalto University) and the Centre for AI Fundamentals in University of Manchester, is searching for postdocs and doctoral students to work on AI fundamentals in exciting projects. The work includes collaboration within ELLIS Institute Finland, the Finnish Center for Artificial Intelligence (FCAI),  with the rest of ELLIS, and researchers from other fields. Samuel Kaski is Professor of Computer Science in Aalto University and Professor of AI in the University of Manchester. He is the Director of  ELLIS Institute Finland and the Finnish Center for Artificial Intelligence. His research group develops machine learning principles and methods focusing on a few key topics, often working with researchers of other fields in new exciting applications (see currently available  topics below). Topics You will join a team developing the next generation of probabilistic and collaborative AI. We study fundamental questions in machine learning, including uncertainty-aware and simulation-based inference, generative modeling, robustness under distribution shift, automatic experimental design, privacy-preserving learning, (inverse) reinforcement learning, computational rationality, and user modelling. Our goal is to develop principled AI methods that are reliable, adaptive, and scientifically useful. The research combines advances in ML foundations with real-world applications in domains such as scientific discovery, healthcare, and design or drugs, materials, systems. By bringing together expertise  in machine learning, statistics, optimization, we tackle challenging interdisciplinary problems that cannot be solved by any single approach alone. Below, we outline the research topics for which we are currently seeking candidates. Multimodal foundation models Key words: multimodal learning, foundation models, human-aligned fine-tuning, fine-tuning for downstream tasks, test-time adaptation You will join a research team developing next-generation multimodal foundation models that can reason across text, images, video, and 3D molecular design and robotic environments. The research is conducted within an EU-funded European AI initiative ELLIOT. Our goal is to make these systems more grounded, adaptable, efficient, and aligned with human goals and feedback. The work combines fundamental advances in multimodal representation learning with practical questions of deploying large-scale AI systems in dynamic real-world settings. Depending on your interests, you may work on topics such as large-scale multimodal training, test-time adaptation under distribution shift, efficient model distillation and adaptation, retrieval-augmented learning, or alignment through human feedback, preference, and interaction. Out-of-Distribution Deployable Machine Learning Key words: out-of-distribution generalization, distribution shift, active learning, human-in-the-loop learning, probabilistic modelling, sequential experimental design, collaborative AI, decision support We develop machine learning methods that remain reliable when deployed outside their training conditions. A central challenge in modern AI is that real-world environments differ from the data that models were trained on, leading to failures caused by distribution shifts, hidden confounders, and incorrect assumptions. Our ERC AdG-funded research addresses these challenges by combining probabilistic machine learning, adaptive inference, and human-collaborative AI. Your work will focus on developing algorithms and frameworks that enable models to adapt to new environments, learn efficiently from limited feedback, and support human decision-making under uncertainty. Depending on your interests, the research may involve out-of-distribution generalization, domain adaptation, active learning, learning from expert feedback, sequential experimental design, collaborative AI systems, or probabilistic approaches to robust deployment. The project combines foundational ML research with opportunities to collaborate closely with leading application-domain experts and international research partners. Collaborative AI Key words: collaborative AI, human–AI interaction, decision support, human-in-the-loop learning, uncertainty-aware AI, interactive machine learning, computational rationality, AI-assisted discovery You will join a research team developing collaborative AI systems that work effectively with people in complex decision-making and problem-solving tasks. Our goal is to build AI methods that can interact naturally with users, reason under uncertainty, adapt to human preferences and expertise, and support reliable human decision-making. The research combines machine learning, probabilistic modelling, cognitive modelling, and interactive AI to develop systems that complement rather than replace human intelligence. You may work on topics such as human-in-the-loop learning, uncertainty-aware decision support, preference learning, adaptive interaction, AI-assisted scientific discovery, computational rationality, or collaborative reasoning between humans and AI systems. The work addresses both foundational questions in human-centered machine learning and practical challenges in deploying collaborative AI in real-world environments. The work offers opportunities to collaborate with leading international researchers and application-domain experts in areas including healthcare, sciences, and intelligent decision support. Fundamental and Applied Machine Learning Research Key words: machine learning, probabilistic modelling, generative AI, representation learning, optimization, trustworthy AI, adaptive systems, AI for science We are also looking for researchers interested in tackling ambitious open problems in machine learning beyond the themes listed above. Our group works on a broad range of topics spanning probabilistic modelling, generative AI, adaptive and interactive learning systems, trustworthy AI, and AI methods for scientific discovery and decision-making. We are particularly interested in research that combines strong methodological foundations with the potential for high real-world impact - and the impact can come at different time horizons. Depending on your background and interests, your work may involve developing new machine learning principles, scalable inference and optimization methods, robust and uncertainty-aware AI systems, generative models, representation learning methods, or novel applications of AI in science, healthcare, and intelligent systems. We encourage interdisciplinary research and collaboration across machine learning, statistics, cognitive science, and application domains. The position offers considerable freedom to shape research directions while contributing to a collaborative and internationally connected research environment. Your experience and ambitions We expect the candidates to have a solid background in the mathematics/statistics/computer science needed in machine learning, and hold or be close to getting a relevant doctoral degree for a postdoctoral researcher and a master degree for a doctoral student researcher. Previous experience in the application fields and cognitive science is an advantage. Capability of both independent work and teamwork, and excellent written and spoken English are necessary. We provide 1) RESEARCH ENVIRONMENT You will work in Professor Samuel Kaski’s research group in ELLIS Institute Finland (Probabilistic Machine Learning Group) or the UK (Centre for AI Fundamentals). We design collaborations as we go, according to what the research needs. Collaborators include but are not restricted to the other groups in ELLIS Institute Finland, the Finnish Center for Artificial Intelligence (FCAI), other sites of the European Laboratory for Learning and Intelligent Systems (ELLIS) and Centre for AI Fundamentals of the University of Manchester and a number of excellent researchers in other fields in our applications. 2) JOB DETAILS Postdoc positions are typically made for up to three years, with option for renewal; doctoral-student positions start with a two-year contract and continue with a second two-year contract after a check-point. Starting dates are flexible and all positions are negotiated on an individual basis. We are strongly committed to offering everyone an inclusive and non-discriminating working environment. We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other groups underrepresented in the field. All our positions are fully funded and the salary is based on the Finnish universities’ pay scale. The starting salary depends on the level of the position and the previous experience and is typically starting from 4200€ for postdocs and 3100€ for doctoral students, increased as the experience grows. All employees have access to occupational health care services and are covered by the Finnish national health insurance system. Ready to apply? Submit your application through Aalto recruitment system Workday by the button below “apply now”. The deadline for applications is  June 28 2026 at 23:59 Finnish time. You can either submit a separate application to the University of Manchester (the application link will be added in early June) if you want to be considered for both locations, or a single application if you want to be considered only for that specific location. Required attachments Cover letter (1–2 pages).  CV List of publications (please do not attach full copies of publications) A transcript of doctoral study and earlier studies (especially for doctoral students) The degree certificate of your latest degree. If you don’t yet have a PhD degree for a postdoctoral researcher position or a Master degree for a doctoral student position, a plan of completion must be submitted. Contact details of two senior academics who can provide references. We will contact your referees if we need recommendation letters. All materials should be submitted in English in a PDF format. Note: You can upload max. five files to the recruitment system, each max. 5MB. Please note: Aalto University’s employees and visitors should apply for the position via our internal system Workday -> find jobs (not external aalto.fi webpage on open positions) by using their existing Workday user account. Contacts: Coordinator Fang Wang (fang.wang@aalto.fi) More Information We are part of ELLIS Institute Finland, which is a leading research environment building on Finland’s strong track record in machine learning research, including work at the Finnish Centre for Artificial Intelligence FCAI. ELLIS Institute Finland is part of the European ELLIS network and operates in close partnership with Finnish universities, RDI organizations, and industry, forming a vibrant AI and machine learning ecosystem. The Institute offers excellent computational resources - its own resources and through CSC, including access to the LUMI supercomputer and the European AI Factory, and is located in Otaniemi, Espoo, within a dynamic research and innovation hub. Aalto University is a community of bold thinkers where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto has six schools with 14 000 students and a staff of 5000, of which more than 400 are professors. Our main campus is located in Espoo, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The Department of Computer Science is an internationally-oriented community and home to world-class research in modern computer science, combining research on foundations and innovative applications. With over 40 professors and more than 450 employees from 50 countries, it is the largest department at Aalto University and the leading computer science research unit in northern Europe. Computer science research at Aalto University ranks high in several international surveys (7th in Europe and 1st in the Nordics (NTU 2023); and 88th worldwide in Times Higher Education subject ranking 2025). 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