项目介绍

2025年1月23日

Are you interested in accelerating computational linear algebra with machine learning, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a PhD position at the Department of Information Technology, Uppsala University.

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research and the Centre for Interdisciplinary Mathematics.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University’s third largest department, with 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material science, and safety and security. We have a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, Imperial College, University of Tübingen, University of Sydney, and Aalto University. We strive for all PhD students to get a solid international experience during their PhD.

Project description
In this project, the doctoral student will explore the potential for accelerating sparse linear algebra with machine learning, specifically graph neural networks. Sparse linear algebra is pivotal in solving large-scale computational problems but requires experts to carefully craft algorithms on a case-by-case basis. Our approach provides an alternative, more accessible, route to highly efficient solvers with the potential to have a major impact throughout science and engineering. The underlying idea is to leverage the inherent graph structure of sparse matrices to create graph neural networks that learn task-adapted algorithms from a dataset of computational problems. Through this integration of machine learning with sparse linear algebra, we hope to unlock unprecedented efficiency and scalability in solving sparse linear systems.

The exact details of the project will be decided in a dialogue between the student and supervisor. Potential directions include (1) developing techniques for learning task-adapted iterative methods for sparse matrices, (2) developing techniques for learning task-adapted multilevel graph representations, and (3) scaling these techniques to realistic, large-scale computational problems. There are ample opportunities for spearheading method development through our collaborations in radiotherapy, medical imaging, additive manufacturing and battery chemistry.

Duties
The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.

Requirements
Entry requirements for doctoral education are regulated in the Higher Education Ordinance. To meet the general entry requirements for doctoral studies, you must:

• hold a Master’s (second-cycle) degree inengineering physics, electrical engineering, machine learning, data science, computer science, applied mathematics or in a similar field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject’s general study plan. 

We are looking for candidates with:

• a strong interest in developing new methods for computational mathematics and machine learning,
• excellent study results,
• high proficiency in programming (preferably in Python),
• good communication skills with sufficient proficiency in oral and written English,
• personal characteristics such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving.

Additional qualifications
Coursework or other experiences with the following subjects are valued: linear algebra, calculus, numerical linear algebra, optimization, statistical machine learning, deep learning, and software engineering.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University’s rules and guidelines.

Application
The application must contain:

1. A cover letter (max 2 pages), in English, briefly describing your motivation for applying for this position and the earliest possible employment date. The cover letter should include the heading Suitability for this position, containing a self-assessment on why you would be the right candidate for this position.
2. A curriculum vitae (CV).
3. A copy of relevant grade documents (translated into Swedish or English).
4. The Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents.
5. Contact information for two references (names, emails and telephone number) and up to two letters of recommendation.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %.Starting date August 18, 2025 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Jens Sjölund, jens.sjolund@it.uu.se, +46 18 471 78 40.

Please submit your application byMarch 28, 2025, UFV-PA 2025/172.

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