PhD scholarship in Machine Learning using Tensor Networks - DTU Compute

Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges both within quantum physics and the life-sciences?

As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning by use of tensor network representations. In particular, you will contribute in pushing the boundaries of 

• Scalability, drawing upon recent large-scale TN capabilities in physics.
• Reliability, exploring uncertainty quantification and robust inference in machine learning
• Explainability, leveraging identifiability and unique recovery explored using tensor decompositions within the life-science domain.

A Tensor Network (TN) is a data structure for representing high-dimensional arrays (tensors) in a low-rank format as a sequence of smaller cores which can be stored and manipulated efficiently. TNs provide promising tools for large-scale machine learning, as they allow for exponential savings in memory and processing time, while often allowing for explainable structure extraction. However, key obstacles remain, preventing their widespread use. These include limitations in terms of reliable large-scale inference, uncertainty quantification, as well as efficient TN structure identification, assessment, and interpretation. You will address these obstacles and demonstrate how the developed tools can address important challenges within quantum physics as well as large-scale life-science data modeling.

Machine learning, programming experience and a curious mind-set 
You are fascinated by machine learning and how computers can learn from data and you have a strong interest in the mathematical foundation of machine learning models. In this position, you will be responsible for developing novel tensor network-based machine learning methodologies enabling new approaches for solving machine learning problems including reinforcement learning problems. You will thereby leverage tensor networks as a computationally efficient and expressive framework that provides a complimentary generic modeling framework to deep learning.

Specifically, we are looking for a profile with the motivation and drive needed for making a difference that matters. You must bring an open mindset and like to create results via collaboration with multiple people working on similar problems with different professional and cultural backgrounds. You are therefore a talented, self-motivated, and team-oriented person who enjoys working on the theoretical foundation of machine learning. In particular, your CV comprises:

• A strong relevant background within machine learning and mathematics. 
• Extensive experience programming machine learning models. 
• An active interest or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, physics and life-science data modeling.
• You must be fluent in English, both speaking and writing and possess excellent communication skills.

As part of the Danish Ph.D. program, you will follow a number of Ph.D. courses as well as take part in teaching and supervision of students. The PhD further includes interesting opportunities for an external research stay.

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.

You will be supervised by Professor Morten Mørup at DTU Compute, Section for Cognitive Systems and also closely collaborate with Prof. Rasmus Bro and Assoc. Prof. Michael Kastoryano at the University of Copenhagen Department of Food Science and Department of Computer Science respectively.  The position is financed by the Novo Nordisk Foundation Data Science Collaborative Grant “Tensor Networks for Data Science”.

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. 

Assessment
The assessment of the applicants will be made by Professor Morten Mørup (DTU Compute).

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. This is a full-time position with starting date 1 October 2025 or according to mutual agreement. 

You can read more about career paths at DTU here.

Further information 
Professor Morten Mørup (DTU Compute) at mmor@dtu.dk.

You can read more about DTU Compute at 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. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” 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 1 August 2025 (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)
• Curriculum vitae 
• 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 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. 

Section for Cognitive Systems
The position is in the Section for Cognitive Systems at DTU Compute, the Technical University of Denmark, which is a top Danish machine learning group. Both salary and working conditions are excellent. The group is a down-to-earth and fun place to be. Most group members live in Copenhagen, which is often named as the best city in the world to live, and for good reasons. It's world renowned for food, beer, art, music, architecture, the Scandinavian "hygge", and much more. Parental leave is generous and child-care is excellent and cheap. You can read more at Section for Cognitive Systems - DTU Compute.

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.