PhD Student in Machine Learning

Popis:

Please note that this is an abbreviated version of the job posting. To view the full posting, please click "Apply here" or visit Uppsala University's website for job openings: https://www.uu.se/om-uu/jobba-hos-oss/lediga-jobb Are you interested in developing mathematically sound methods for uncertainty quantification in deep learning, with particular focus on large language models for applications in healthcare? Do you want an employer that invests in sustainable employee relations and offers secure, beneficial working conditions? You are welcome 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. The department is currently Uppsala University's third largest institution with just over 350 employees, including 120 faculty members and 120 PhD students. Approximately 5000 undergraduate students take one or more courses at the department each year. More information about us can be found on the Department of Information Technology's website. About the DDLS Research Program The PhD position is part of the national research program DDLS. Data-driven life science (DDLS) uses data, computational methods, and artificial intelligence to study biological systems and processes at all levels – from molecular structures and cellular processes to human health and global ecosystems. SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and educate the next generation of data-driven life science researchers and to create globally leading expertise in computational and data science in Sweden. The program is financed with a total of 3.3 billion kronor over 12 years from the Knut and Alice Wallenberg Foundation (KAW). In 2026, the DDLS research school will expand by recruiting 25 academic and 7 industrial PhD students. Over the course of the program, more than 260 PhD students and 200 postdoctoral researchers will be part of the research school. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biological diversity, precision medicine and diagnostics, and epidemiology and infectious disease biology. For more information, see: https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Do you want to be part of this change? Then you are welcome to participate in this unique program! Project Description Large language models (LLMs) enable extraction of clinical information from unstructured medical text. However, current LLM-based methods often lack principled uncertainty quantification, which limits their reliability in healthcare applications. The project aims to develop mathematically sound methods for uncertainty quantification in deep learning, with particular focus on large language models, based on probability theory, statistical inference, and probabilistic modeling. The focus is on quantifying and evaluating uncertainty in predictions derived from medical records and integrating these uncertainties into subsequent probabilistic time-to-event models. Applications will focus on prostate cancer and will use large-scale clinical registry data as well as unstructured medical text. Duties The PhD student will primarily engage in their own research training. Other duties at the department, including teaching and administrative work, may be included within the scope of the employment (max 20%). Qualification Requirements Eligibility for doctoral education requires: a degree at advanced level in applied mathematics, applied statistics, engineering physics, physics, machine learning, or in a similar field, or completion of at least 240 higher education credits, of which at least 60 higher education credits at advanced level including an independent project of at least 15 higher education credits, or acquisition of substantially equivalent knowledge in another way. The university may grant exceptions to the basic eligibility requirement for an individual applicant if there are special reasons. (7 chap. 39 § HF). For special eligibility requirements, see the course plan for the subject. We are seeking candidates with: interest in methodology development within applied mathematics and statistics, interest in uncertainty-aware machine learning, good communication skills and sufficient knowledge of English, both spoken and written, creativity, attention to detail, and a structured approach to problem-solving. Strong knowledge of linear algebra, probability theory, and analysis is required. Good programming skills are also required. Desirable/Meriting Experience Experience in one or more of the following areas is advantageous: Bayesian statistics mathematical modeling probabilistic machine learning deep learning large language models Regulations for PhD students are found in the Higher Education Ordinance 5 chap §§ 1-7 and in the university's rules and guidelines. Application The application must contain: a personal letter (maximum 1 page) where you explain how you meet the qualification requirements, explain why you are applying for this position, and your estimated earliest start date; a CV; degree certificates and transcript with grades (translated into English or Swedish); a thesis report (or draft thereof, and/or other self-produced technical or scientific text), publications, and other relevant documents; references with contact information (name, email, and phone number) and up to two letters of recommendation. About the Employment The position is fixed-term, according to HF 5 chap § 7. The position is full-time. Start date October 1, 2026 or by agreement. Placement location: Uppsala. Information about the position is provided by: Assistant University Lecturer Sara Hamis, e-mail: [email protected]. Welcome to submit your application no later than July 31, 2026, UFV-PA 2026/1935.

Přehled

Typ

job

Stav

active

Viditelnost

public

Město

Uppsala

Adresa

Regementsvägen 10

GPS

59.839570068472, 17.646119822441kopírovatotevřít v ▾

Email

[email protected]

Telefon

0184710000

Zobrazení

5

Publikováno

9. 6. 2026

Upraveno

21. 6. 2026

Parametry