PhD student in Scientific Computing focusing on Machine Learning Theory

Uppsala universitet (Uppsala University); Uppsala; On-site; SEK 800 000 - 1 000 000; Full time

Job summary

Uppsala University invites applications for a PhD position in scalable Bayesian learning from noisy data. The successful candidate will be part of a significant NEST initiative, contributing to cutting-edge research in scientific computing with opportunities for collaboration both nationally and internationally.

Qualifications

• Un diplôme de Master en informatique ou un domaine connexe est requis.
• Une forte motivation pour l'apprentissage basé sur des données est essentielle.
• Des résultats académiques excellents sont attendus.

Responsibilities

• Le doctorant doit s'investir dans la recherche et suivre des cours de troisième cycle.
• Quelques tâches d'enseignement et d'administration peuvent être impliquées.

Skills

Optimisation

Machine Learning

Bayesian Inference

Education

Master’s degree in related field

Tools

Python

Job description

2025-06-16

Are you interested in working in the area of computational learning theory, 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, favorable working conditions? We welcome you to apply for a PhD position at Uppsala University.

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, have around 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.

We are offering a PhD student position exploring scalable Bayesian learning from noisy data. The student will form a part of a new NEST initiative funded by the Wallenberg National Program for Data-Driven Life Science (DDLS), and the Wallenberg AI, Autonomous Systems and Software Program (WASP). The NEST project, Time-Resolved Imaging and Multi-Channel Evaluation of Cellular Dynamics (TIMED), is a 5-year collaboration between researchers at Uppsala University, Chalmers University of Technology, and Karolinska Institute.

The position is hosted by the Division of Scientific Computing (TDB) within the Department of Information Technology. As one of the world’s largest focused research environments in Scientific Computing, the research and education has a unique breadth, with large activities in scientific computing areas such as mathematical modeling, development and analysis of algorithms, machine learning theory, optimization, scientific software development and high-performance computing. The division currently hosts 20 PhD students, with more than 90 doctorates awarded. PhD alumni from the division are successful practitioners in the field of scientific computing and related areas, in industry as well as in academia.

The division is also an important part of the eSSENCE strategic collaboration on e-science, and the Science for Life Laboratory (SciLifeLab) network. SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent. The successful candidate will be hosted by the Scientific Machine Learning group at TDB. The group specializes in developing theory, methods and software for enabling data driven science. Relevant to this position, the group is active in exploring machine learning theory, large-scale optimization, Bayesian inference and uncertainty-aware learning, and other aspects of statistical learning. We have a wide network of collaborators, and there will be opportunities to work together with excellent researchers within Sweden and abroad. The project is part of the DDLS and WASP initiatives, which aim to position Sweden at the forefront of computational life sciences and AI research.

Project description

The successful candidate will join us in developing principled foundations of scalable learning from noisy datasets, with particular focus on live-cell imaging data. We will study probabilistic guarantees and generalization bounds for Bayesian learners under distributional shifts, model misspecification, and limited samples. We will also explore deep learning architectures integrating probabilistic priors, or approximate Bayesian inference to enhance robustness and interpretability. We will apply and validate the developed machine learning methods on live-cell imaging data generated in our TIMED NEST environment, offering significant opportunities for collaboration. Technical keywords for the position include computational learning theory, robust learning, large-scale optimization, Bayesian machine learning.

Duties

A Ph.D. student is expected to devote their time to graduate education mainly, which involves conducting research within the doctoral project, and taking graduate courses. The rest of the duties may involve teaching at the Department, including also some administration, to at most 20 %.

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 in computer science, computational science, applied mathematics, engineering physics, machine learning, data science, or a related 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 optimisation, machine learning, and Bayesian inference,
• good communication skills with sufficient proficiency in oral and written English,
• excellent study results,
• programming proficiency (preferably in Python),
• personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving.
  
  

Additional Qualifications

Experience and courses in one or more of these subjects are valued: optimisation, probabilistic machine learning, linear algebra and deep learning.

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 Include

• a statement (at most 2 pages) of the applicant’s motivation for applying for this position, including a self-assessment on why you would be the right candidate for this position;
• a CV;
• degrees and transcript of records with grades (translated to English or Swedish);
• the Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents;
• references with contact information (names, emails and telephone number) and up to two letters of recommendation.
  
  

Applicants who meet at least one of the entry requirements are strongly encouraged to apply. All applicants should state their earliest possible starting date.

About The Employment

The employment is a temporary position according to the Higher Education Ordinance chapter 5

• 7. Scope of employment 100 %. Starting date 1 September 2025 or as agreed. Placement: Uppsala.
  
  

For further information about the position, please contact: Assistant Professor Prashant Singh, e-mail: [e-mail]; Head of Division Elisabeth Larsson, e-mail: [e-mail].

Co-supervising team: Professor Ola Spjuth, Assistant Professor Rocío Mercado Oropeza, Group Leader Brinton Seashore-Ludlow, Associate Professor Ashkan Panahi.

Please submit your application by 21 July 2025, UFV-PA 2025/2005.

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden.

Uppsala University is a broad research university with a strong international position. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all of our 7,600 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of Sweden’s most exciting workplaces.

Read more about our benefits and what it is like to work at Uppsala University

https://uu.se/om-uu/jobba-hos-oss/

The position may be subject to security vetting. If security vetting is conducted, the applicant must pass the vetting process to be eligible for employment.

Please do not send offers of recruitment or advertising services.

Submit your application through Uppsala University's recruitment system.

Placering: Department of Information Technology Omfattning: Full time Sysselsättningsgrad: 100 % Anställningsform: Temporary position Lön: Fixed salary Antal lediga befattningar: 1 Ort: Uppsala Fackliga företrädare:

• ST/TCO [e-mail]
• Seko Universitetsklubben [e-mail]
• Saco-rådet [e-mail]
  
  

Referensnummer: UFV-PA 2025/2005 Sista dag för ansökan: 21 juli 2025 Sök jobbet