PhD Fellowships in Machine Learning - University of Copenhagen
Department of Computer Science, Machine Learning Section invites applications for PhD fellowships. At least five open positions are being advertised in this call. The projects are detailed below. Start date 1 February 2026 or as soon as possible hereafter.
Projects:
Green Computation Scheduling
Computation has become the backbone of modern society, but it consumes a considerable amount of energy. The project aims to reduce carbon emissions from computation by designing algorithms to schedule computation at times of low carbon intensity of the electricity supply. The challenge is that due to high intermittence of green energy sources and multitude of independent consumers, prediction of carbon intensity is challenging. Even if it was possible to predict the supply, independent attempts to exploit low carbon energy would lead to demand spikes, invalidating the predictions. We aim to address this challenge by building on recent advances in online and reinforcement learning in adversarial environments, and further advancing this field of research. Further details about the project are available here. Candidates applying for this position are expected to have solid theoretical background and mathematical skills. Background in online learning, bandits, and theoretical reinforcement learning is an advantage. The project will be supervised by Yevgeny Seldin and Raghavendra Selvan. For inquiries concerning this project, please, contact Yevgeny Seldin .
Resource efficiency for generative AI
In this project, we will broadly investigate resource-efficient Large Language Models (LLMs) and their effect on the sustainability of AI. This can be at the level of developing novel algorithms, training, learning and prompting paradigms, or hardware optimization techniques that can result in reductions in the resources required when developing and deploying LLM pipelines.The interplay of resource efficiency with the broader sustainability of Generative AI (in terms of safety, fairness, and access) will be of particular interest. For more details about the project contact Christina Lioma .
Candidates applying for this position must have strong skills in mathematics and ML, with a drive towards advancing the UN sustainable development goals. The project will be supervised by Christina Lioma, Maria Maistro and Raghavendra Selvan. For inquiries concerning this project, please, contact Christina Lioma .
Sustainable Machine Learning for Earth Observation
The PhD project will broadly investigate sustainable machine learning (ML) methods for Earth observation. This can include development of novel algorithms, learning paradigms, or hardware optimization techniques that can result in reductions in the resources required when developing and deploying ML pipelines for Earth observation. Specific areas of high interest include the development of high-resolution models from low-resolution labels and multi-modal data, as well as the creation of tailored quantization, data selection, and data condensation strategies specifically for Earth Observation data. The position is part of the TreeSense: Centre for Remote Sensing and Deep Learning of Global Tree Resources, and the candidate would closely work with collaborators at the Department of Geosciences and Natural Resource Management. Candidates applying for this position must have strong skills in mathematics and ML, with a drive towards advancing the UN sustainable development goals. The project will be supervised by Christian Igel, Ankit Kariryaa and Nico Lang. For inquiries concerning this project, please contact Christian Igel or Ankit Kariryaa .
Resource-Efficient Machine Learning for Radiotherapy
AI models will enable more precise and continuous adaptation of radiotherapy treatments to improve patients' prognosis. This project will develop algorithms to detect changes that occur during treatment and create individualised models that incorporate patients' medical history. We will develop new methods that can utilise the information that is available from existing treatment scans, but which currently cannot be fully utilised, The project’s goal is not only to improve treatment for the approximately 14,000 Danish cancer patients who receive radiation therapy each year, but also to develop solutions that can be scaled globally - particularly to benefit low- and middle-income countries. This is part of the AIM@CANCER project funded by the Novo Nordisk Foundation’s Grand AI Challenge. The project will be supervised by Jens Petersen and Raghavendra Selvan. For inquiries concerning this project, please contact Jens Petersen (michaelrussellphupfolson@snyder.dkcastro.dkho.dkdi.ku.dk) or Raghavendra Selvan (tracysmithalvarezdavidraghavkluna@di.ku.dklawrence.dkshelton.dk).
Machine Learning for Design of Sustainable Food Processing
Currently, plant ingredients are often refined to almost molecular purity - and then combined again to create structured foods. This isolation is resource intensive, and the removal of fibre and micronutrients can compromise the nutritional value. This can be mitigated by applying milder forms of processing that do not fully refine ingredients and leave some of the native structure of the plant material intact. These less refined ingredients however exhibit complex behaviour, and we therefore need machine learning to direct the experimental data generation that will be carried out by other team members in the project.
Learning meaningful representation spaces that model the complex space of ingredients and their properties can be immensely useful. These representation spaces can be informed by multiple modalities of data, spanning time-series data, microscopy images, rheological measurements, and so on. Integrating these modalities into common representation spaces can help in the development of more sustainable food. The project will be in collaboration with Department of Food Sciences, UCPH. Further details about the project are available here.The project will be supervised by Christian Igel, Remko Boom and Raghavendra Selvan. For inquiries concerning this project, please contact Christian Igel , Remko Boom or Raghavendra Selvan .
Important: When applying, please write down Yevgeny Seldin (Position 1), Christina Lioma (Position 2), Ankit Kariryaa (Position 3), Jens Petersen (Position 4), or Christian Igel (Positions 5) in the Principal Supervisor field to indicate which supervisor you are applying to. The Principal Supervisor indicated by you will review your application. If relevant, you can express interest in several projects in your cover letter, and we will consider it internally. Applications that have not indicated a Principal Supervisor will not be reviewed.
Our section and research - and what do we offer?
The Machine Learning Section is part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen, and the ELLIS Unit Copenhagen (https://ellis.eu/). The department is heading 2 centers within Artificial Intelligence: the SCIENCE AI Center and the Pioneer Center within Artificial Intelligence. The University of Copenhagen was founded in 1479 and is the oldest and largest university in Denmark. It is ranked as the best university in Scandinavia and as one of the top places in Europe.
The Department of Computer Science offers a friendly and thriving international research and working environment with opportunities to build up an internationally competitive research profile. Copenhagen is constantly rated as one of the 10 most livable cities in the world. Family life is supported by publicly subsidised daycare and health care systems, dual career opportunities, generous maternity and paternity leaves, and six weeks of paid annual vacation. International candidates may find information on living and working in Denmark here. Useful information is also available at The International Staff Mobility office (ISM) at the University of Copenhagen (link). ISM offers a variety of services to international researchers coming to and working at the University of Copenhagen.
The PhD programme
Depending on your level of education, you can undertake the PhD programme as either:
Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.
Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master´s degree – but you have an education equivalent to a Danish bachelors´s degree.
Option A: Getting into a position on the regular PhD programme
Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, and Statistics. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Terms of employment in the regular programme
Employment as a PhD fellow is full time and for a max. 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.
Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. Depending on seniority, the monthly salary starts at approximately 30,800 DKK/Roughly 4.100 EUR (April 2025 level) plus pension. The position is covered by the Protocol on Job Structure.
Option B: Getting into a position on the integrated MSc and PhD programme
Qualifications needed for the integrated MSc and PhD programme
If you do not have an education equivalent to a relevant Danish master´s degree, you might be qualified for the integrated MSc and PhD programme, if you have an education equivalent to a relevant Danish bachelor´s degree. Here you can find out, if that is relevant for you: General assessments for specific countries and Assessment database.
Terms of the integrated programme
To be eligible for the integrated scholarship, you are (or are eligible to be) enrolled at one of the faculty’s master programmes in Computer Science.
Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree programme.
The duration of the integrated programme is up to five years, and depends on the amount of credits that you have passed on your MSc programme. For further information about the study programme, please see: www.science.ku.dk/phd, “Study Structures”.
Until the MSc degree is obtained, (when exactly two years of the full 3+5 programme remains), the grant will be paid partly in the form of 48 state education grant portions (in Danish: “SU-klip”) plus salary for work (teaching, supervision etc.) totalling a workload of 150 working hours per year.
A PhD grant portion is currently (2025) DKK 7.086 before tax.
When you have obtained the MSc degree, you will transfer to the salary-earning part of the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.
Responsibilities and tasks in both PhD programmes
- Complete and pass the MSc education in accordance with the curriculum of the MSc programme (ONLY when you are attending the integrated MSc and PhD programme)
- Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
- We are looking for the following qualifications:
- Professional qualifications relevant to the PhD project
- Strong interest in the relevant topics
- Academic background (e.g. courses taken) in the relevant topics
- Good English language skills
- (Optional) Relevant publications
- (Optional) Relevant work experience
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW.
Please include:
Motivated letter of application clearly mentioning the project you are applying for (max. two pages)
Curriculum vitae including information about your education, experience, relevant courses, language skills, names and email addresses of three referees, and other skills relevant for the position
Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
Publication list (if possible)
Remember to indicate the name of Principal Supervisor in the application!
Application deadline:
The deadline for applications is 30 September 2025 23:59 CET.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.
The further process
After the deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified whether you will be passed for assessment.
The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held in the first half of November 2025.
Questions
For specific information about the PhD fellowship, please contact the relevant supervisors whose emails are provided above.
General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/.
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.