Postdoctoral Position in Mathematics (Royal Institute of Technology)

Institution: KTH Royal Institute of Technology, School of Engineering Sciences Reference Number: PA-2025-3874 Department: Department of Mathematics Field: Applied and Computational Mathematics Employment Type: Full-time, Temporary (PhD position) Starting Date: According to agreement Application Deadline: 09 January 2026 Project Description Focus: Bayesian inverse problems and generative AI models Goal: Explore how generative AI models, particularly diffusion models, can serve as priors in Bayesian inverse problems. Objectives: Develop efficient Monte Carlo methods for sampling complex posterior distributions Contribute to the theoretical foundations and practical applications of Bayesian AI Funding: Swedish Research Council Research Environment: The PhD student will be part of KTH’s Department of Mathematics, engaging in both theoretical and computational research. Additional Duties: Limited departmental tasks, mainly teaching at the undergraduate level. Supervision: Professor Jimmy Olsson Eligibility & Requirements General Requirements: Master’s degree (or equivalent, at least 240 higher education credits with 60 at second-cycle level) English proficiency equivalent to English B/6 Special Requirements for this Program: Second-cycle education focused on mathematics, applied mathematics, or computational mathematics Interest in Bayesian statistics, applied probability, computational mathematics, machine learning, and generative AI Selection Criteria: Ability to work independently and collaboratively Professional approach and perseverance Ability to analyze and tackle complex problems Knowledge in mathematics, computational methods, and AI Employment Information PhD positions at KTH: maximum 4 years full-time Initial employment: up to 1 year, renewable for up to 2 years at a time Licentiate track: total 2 years full-time Employment includes employee benefits and monthly salary according to KTH’s doctoral student salary agreement Union & Doctoral Network: Students’ union and doctoral section contacts available Application Requirements Applications must include: CV with relevant experience Application letter (max 2 pages) describing research interests, academic background, and career goals Copies of diplomas and transcripts (certified, translated to English or Swedish if necessary) Representative publications or technical reports (with summaries and web links for long documents) Submission: Apply through KTH’s recruitment system; ensure complete applications by the deadline Contact Professor Jimmy Olsson – jimmyol@kth.se If you apply for this position please say you saw it on Computeroxy

Institution: KTH Royal Institute of Technology, School of Engineering Sciences
Reference Number: PA-2025-3874
Department: Department of Mathematics
Field: Applied and Computational Mathematics
Employment Type: Full-time, Temporary (PhD position)
Starting Date: According to agreement
Application Deadline: 09 January 2026

Project Description

Focus: Bayesian inverse problems and generative AI models
Goal: Explore how generative AI models, particularly diffusion models, can serve as priors in Bayesian inverse problems.
Objectives:
- Develop efficient Monte Carlo methods for sampling complex posterior distributions
- Contribute to the theoretical foundations and practical applications of Bayesian AI
Funding: Swedish Research Council
Research Environment: The PhD student will be part of KTH’s Department of Mathematics, engaging in both theoretical and computational research.
Additional Duties: Limited departmental tasks, mainly teaching at the undergraduate level.
Supervision: Professor Jimmy Olsson

Eligibility & Requirements

General Requirements:

Master’s degree (or equivalent, at least 240 higher education credits with 60 at second-cycle level)
English proficiency equivalent to English B/6

Special Requirements for this Program:

Second-cycle education focused on mathematics, applied mathematics, or computational mathematics
Interest in Bayesian statistics, applied probability, computational mathematics, machine learning, and generative AI

Selection Criteria:

Ability to work independently and collaboratively
Professional approach and perseverance
Ability to analyze and tackle complex problems
Knowledge in mathematics, computational methods, and AI

Employment Information

PhD positions at KTH: maximum 4 years full-time
Initial employment: up to 1 year, renewable for up to 2 years at a time
Licentiate track: total 2 years full-time
Employment includes employee benefits and monthly salary according to KTH’s doctoral student salary agreement

Union & Doctoral Network:

Students’ union and doctoral section contacts available

Application Requirements

Applications must include:

CV with relevant experience
Application letter (max 2 pages) describing research interests, academic background, and career goals
Copies of diplomas and transcripts (certified, translated to English or Swedish if necessary)
Representative publications or technical reports (with summaries and web links for long documents)