PhD scholarship in Probabilistic Machine Learning for Decision-Making under at Danmarks Tekniske Universitet

Copenhagen, Capital, Denmark -

Full Time

Start Date

Immediate

Expiry Date

03 Aug, 26

Salary

0.0

Posted On

05 May, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Probabilistic Machine Learning, Deep Learning, Active Inference, Python, PyTorch, JaX, Tensorflow, HPC Environments, Sequential Decision-Making, Uncertainty Quantification

Industry

Research Services

Description

Are you interested in probabilistic machine learning, uncertainty quantification, and sequential decision-making? Then this PhD position may be for you. DTU Compute invites applications for a fully funded PhD scholarship with a duration of three years. The PhD project is part of the research grant “Active Inference for Dynamic Pricing”, funded by the Fund of 20th December. The overall aim of the project is to investigate active inference as a principled framework for developing robust methods for sequential decision-making under uncertainty, building on state-of-the-art machine learning methods and architectures. The project will investigate and validate the developed methodology through several real-world applications, including dynamic pricing. Responsibilities and qualifications As a PhD candidate, you will conduct independent and collaborative research on probabilistic machine learning and active inference for sequential decision-making under uncertainty. Your work will involve developing novel models and algorithms, as well as implementing and evaluating them using modern machine learning frameworks. You will investigate the proposed methodology through realistic use cases, including dynamic pricing. The position also includes disseminating research results through publications in leading international journals and conferences, and participating in scientific collaborations. As part of the PhD programme, you will complete relevant coursework and contribute to the academic environment at DTU Compute, including limited teaching and supervision activities. Qualifications: * Strong background in probabilistic machine learning and deep learning * Mathematical maturity * Experience in working for HPC enviroments * Solid programming skills (Python + modern machine learning frameworks like PyTorch, JaX, Tensorflow etc.) You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education [https://www.dtu.dk/english/Education/PhD/Rules]. We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and appointment terms 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. The period of employment is 3 years. Preferred start date is 1September 2026 or according to mutual agreement. You can read more about career paths at DTU here [http://www.dtu.dk/english/about/job-and-career/working-at-dtu/career-paths]. Further information For further information, contact Associate Professor Bjørn Sand Jensen (bjje@dtu.dk [bjje@dtu.dk]) or Associate Professor Michael Riis Andersen (miri@dtu.dk [miri@dtu.dk]) from the section of Cognitive Systems at DTU Compute. You can read more about the DTU Compute at www.compute.dtu.dk/ [https://www.compute.dtu.dk/] and the section at www.compute.dtu.dk/sections/cogsys [https://www.compute.dtu.dk/sections/cogsys]. 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]. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark/before-arrival/phd-relocation-to-denmark]” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. Application procedure Your complete online application must be submitted no later than 19 May 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: * A letter motivating the application (cover letter, max. 1 A4 page) * Curriculum vitae (max. 2 pages) * Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale You may apply prior to obtaining your master's degree but cannot begin before having received it. 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 DTU Compute – Department of Applied Mathematics and Computer Science – is an internationally recognised academic environment with over 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 (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research 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. Read more about us at www.compute.dtu.dk [http://www.compute.dtu.dk]. DTU – For the benefit of society since 1829 DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.

Responsibilities

Conduct independent research on probabilistic machine learning and active inference to develop robust methods for sequential decision-making. Responsibilities include developing novel algorithms, validating them through real-world applications like dynamic pricing, and publishing results in international journals.