PhD student in Scientific Computing focusing on Scientific Machine Learning

Uppsala University

Application deadline

April 2, 2024

PhD student in Scientific Computing focusing on Scientific Machine Learning

Uppsala University, Department of Information Technology

Are you interested in working in the area of scientific 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, 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.

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 classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software development and high-performance computing. The division is currently in an expansive phase in new emerging areas such as cloud and fog computing, data science, and artificial intelligence, where it plays key roles in several new strategic initiatives at the University. The division currently hosts 20 PhD students, with more than 90 doctorates awarded. Several 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. We have a wide network of collaborators, and there will be opportunities to work together with excellent researchers within Sweden and abroad.

Project description
The successful candidate will join us in developing principled foundations of learning from noisy datasets, with opportunities to validate the methods on challenging scientific problems. There will be a strong focus on deep learning and Bayesian methods in the context of the project. We will consider the problem setting of simulation-based inference, where machine learning models are used to learn expressive, informative features from high-dimensional scientific data arising from both, simulations and experiments. We will consider various forms of data, including structured representations such as time series (e.g., studying protein interactions via observed copy numbers) and images (e.g., microscopy). Technical keywords for the position include variational inference, likelihood-free parameter inference, robust learning and large-scale optimization.

A Ph.D. student is expected to devote their time to graduate education mainly. The rest of the duties may involve teaching at the Department, including also some administration, to at most 20%.

Requirements To meet the 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.

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 are essential.

Additional qualifications
Experience and courses in one or more 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.

The application must include: 
1) 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; 
2) a CV;
3) degrees and transcript of records with grades (translated to English or Swedish); 
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) 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 19 August 2024oras agreed. Placement: Uppsala. 

For further information about the position, please contact: Assistant Professor Prashant Singh, e-mail:; Head of Division Emanuel Rubensson,

Please submit your application by 2 April 2024, UFV-PA 2024/616.

Last updated: 2024-02-16

Content Responsible: David Gotthold(