Professor in Probabilistic Machine Learning for Science

Are you an established researcher in probabilistic machine learning, with a passion for developing robust, trustworthy, and explainable AI methods for applications in science and engineering? Then this professor position might be for you. 

We are looking for a new professor to lead research in probabilistic machine learning, with a focus on areas such as deep generative models, Bayesian methods, and uncertainty-calibrated AI. These approaches are crucial for realizing the potential of machine learning in fields like physics, chemistry, and bioinformatics—where practical and reliable methods are needed to drive digitalization forward. 

The professor will play a key role in strengthening DTU Compute’s activities in probabilistic machine learning and will help build a strong research community around applied AI. The position involves leading innovative research, engaging in collaboration with academic and industrial partners, and ensuring high-quality teaching and supervision at all levels. 

Responsibilities and qualifications

You are expected to be part of defining the teaching within the core areas of DTU Compute, including courses on probabilistic machine learning and its engineering applications at the BEng, BSc, MSc, and PhD levels. In addition, there will be an obligation in continuous education on advanced machine learning methods and AI. 

The Section for Cognitive Systems has a strong interest and expertise in probabilistic machine learning, covering both theoretical foundations and engineering applications. We expect you to be motivated by and have experience with advancing the field through methodological research, with a strong track record of publishing in leading machine learning venues (e.g., NeurIPS, ICML, ICLR, AISTATS). You should also be enthusiastic about and have a track record of applying machine learning methods to scientific challenges in areas such as bioinformatics, physics, or chemistry. You will collaborate closely with skilled colleagues across areas such as computational modelling, molecular and materials science, bioinformatics and related applied research, creating opportunities for interdisciplinary innovation and impact. 

In addition to research excellence, we expect active engagement in the international machine learning community. Experience with conference organisation (e.g., chair roles) or other forms of scientific service at leading ML venues is considered an important qualification. 

Securing research funding is essential for maintaining an international leadership position. As a researcher at the interface of probabilistic machine learning and technical sciences at DTU, you will have excellent opportunities to attract funding. We expect you to have experience in attracting funding for your research, with a strong understanding of its societal and scientific impact. 

Application procedure

To apply, please read the full job advertisement via the 'Apply' button above.

Application deadline: 15 April 2026 

Technology for people

DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.

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