Professor in Probabilistic Machine Learning for Science – DTU Compute at Danmarks Tekniske Universitet

Kongens Lyngby, Capital Region of Denmark, Denmark -

Full Time

Start Date

Immediate

Expiry Date

16 Jun, 26

Salary

0.0

Posted On

18 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Probabilistic Machine Learning, Deep Generative Models, Bayesian Methods, Uncertainty-Calibrated AI, Methodological Research, Scientific Application, Teaching, Supervision, Research Funding, Collaboration, Curriculum Development, Leadership, Interdisciplinary Skills, Communication

Industry

Research Services

Description

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 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. DTU employs two working languages: Danish and English. You are expected to be fluent in at least one of these languages, and in time are expected to master both. You will be assessed against the responsibilities and qualifications stated above and the following general criteria: * Documented experience and quality of teaching and curriculum development * Research impact and experience, funding track record and research vision * International impact and experience * Societal impact * Innovativeness, including commercialization and collaboration with industry * Leadership, collaboration, and interdisciplinary skills * Communication skills Salary and terms of employment The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. Further information Further information may be obtained from the Head of Section, Professor Lars Kai Hansen, lkai@dtu.dk [lkai@dtu.dk], +45 4525 3889, or Head of Department, Professor Jan Madsen, jama@dtu.dk [jama@dtu.dk], mobile: +45 6017 1097. You can read more about DTU Compute at www.compute.dtu.dk [http://www.compute.dtu.dk]. If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark]. Application procedure Your complete online application must be submitted no later than 15 April 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: * Application (cover letter) addressed to the President * Vision for teaching and research * CV including employment history, list of publications indicating scientific highlights, H-index and ORCID (see http://orcid.org/ [http://orcid.org/]) * Teaching portfolio [https://learninglab.dtu.dk/courses-and-events/udtu?accordion=8] including documentation of teaching experience * Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here [https://www.dtu.dk/english/about/job-and-career/recruitment-process/academic-assessment-process]. Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position. DTU Compute The Section for Cognitive Systems is part of the Department of Applied Mathematics and Computer Science (DTU Compute) – an internationally recognised academic environment with more than 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things, chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in fundamental science and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition, energy supply, and life science. We collaborate with universities, public and private organisations, and companies in Denmark and abroad, and through DTU’s startup ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has the opportunity to actively participate in the development, and we ensure a continued open, democratic, and inclusive society for the benefit of all. At DTU Compute, we value diversity, inclusion, and a flexible work-life balance. 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.

Responsibilities

The professor will lead innovative research in probabilistic machine learning, focusing on areas like deep generative models and Bayesian methods, while also being responsible for high-quality teaching and supervision across various academic levels. This role involves defining teaching within core areas and building a strong research community around applied AI.