PhD student in Causal machine learning for precision medicine
- Uppsala
- Tillfälligt
- Heltid
This PhD project is part of the interdisciplinary WASP-DDLS NEST project , which has the overarching aim of advancing data-driven multimodal methods to enable true precision diagnostics throughout the breast cancer pathway. AID4BC’s constellation of partners, located at four leading sites in Sweden, is likely the only constellation globally having access to large (>10,000 patients) matched multimodal data across radiology, pathology and molecular profiling and clinical data.Machine learning methods hold the potential to advance precision medicine and clinical decision support for breast cancer diagnostics and treatment. At the same time, these application areas present new challenges for statistical learning methodology. They involve high-stakes decisions with important trade-offs and uncertainties. They are also challenged by the data sampling process which gives rise to distribution shifts when comparing past and future data.This project is focused on research into theory and methods that can address these novel challenges and is motivated by the need to develop machine learning approaches that take into account causal relationships. The aim is to develop new ideas and methods that can tackle the uncertainties and make explicit the relevant clinical trade-offs in both predictive and prescriptive data-driven methods.The candidate is expected to collaborate extensively with clinical experts at the other sites of the AID4BC project, as well as with clinical partners at Uppsala University Hospital, with the ultimate goal of improving patient health outcomes. The details of the project will be decided in a dialogue between the student and supervisor.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 in engineering physics, electrical engineering, machine learning, data science, mathematical statistics, 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.
- a solid mathematical foundation and an interest in statistical learning,
- an interest in interdisciplinary collaboration and willingness to translate new methods into clinical applications,
- 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 a structured approach to problem-solving.
Experience and courses in one or more subjects are valued: estimation theory, statistical machine learning, optimization, signal processing, system identification, or control theory.Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in .Application
The application must contain: * A cover letter (max 2 pages), in English, briefly describing your motivation for applying for this position and why you would be the right candidate. The letter should also specify the earliest possible employment date.
- A curriculum vitae (CV).
- A copy of relevant grade documents (translated into Swedish or English).
- The Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents.
- Contact information for two references (names, emails and telephone number) and up to two letters of recommendation
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date January 12, 2026 or as agreed. Placement: Uppsala.For further information about the position, please contact: Dave Zachariah (dave.zachariah@it.uu.se) or Jens Sjölund (jens.sjolund@it.uu.se).Please submit your application by October 3, 2025, UFV-PA 2025/2618.Are you considering moving to Sweden to work at Uppsala University? .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 Universityhttps://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. Anställningsform: tidsbegränsad anställning | Anställningens omfattning: heltid | Antal lediga befattningar: 1 | Sysselsättningsgrad: 100 | Ort: Uppsala | Län: Uppsala län | Land: Sweden | Referensnummer: UFV-PA 2025/2618 | Facklig företrädare: ST/TCO tco@fackorg.uu.se, ST/TCO tco@fackorg.uu.se, Seko Universitetsklubben seko@uadm.uu.se, Seko Universitetsklubben seko@uadm.uu.se, Saco-rådet saco@uadm.uu.se, Saco-rådet saco@uadm.uu.se, | Publicerat: 2025-08-29 | Sista ansökningsdag: 2025-10-03