Karolinska Institutet, MEB

The Department of Medical Epidemiology and Biostatistics (MEB) is among the largest departments of epidemiology in Europe with special focus on increasing our knowledge of the aetiology of different diseases. Our department consists of researchers, doctoral students, biostatisticians, data collectors and database administrators as well as administrative personnel, in total some 250 staff. The department is situated at campus Solna. Further information can be found at ki.se/meb

A two-year postdoctoral research position (with possibility for extension) is available within the biostatistics group at the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet (https://ki.se/meb), Stockholm. The research project is part of a cross-university collaboration within a national e-science program and will be carried out in collaboration with researchers both at MEB and at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology (https://www.kth.se/rpl), Stockholm. The biostatistics group at MEB, KI comprises 4 professors, 3 senior lecturers, 5 PhD-level statisticians, 4 masters-level statisticians, and 7 doctoral students. The group is involved in a wide variety of research projects, including population-based cohort and case-control studies, twin and family studies, survival analyses, predictive modeling, genetic epidemiology and bioinformatics. The collaborating group at RPL, KTH comprises one professor, 6 doctoral students, two research engineers, and a varying number of master students. The group is targeted towards probabilistic machine learning and probabilistic deep learning, with a wide range of collaborations in medicine, veterinary science, autonomous vehicles, performing arts, and e-science.

The aim of this project is to develop algorithms that can efficiently fit (i) complex, latent variable models to large-scale Swedish health and population register data, such as data from the Swedish Multi-generation Register or from the Twin Register and (ii) complex latent variable models to medical image data. The exact topic of the position will depend on the successful applicant's background and interests.

There are a number of approaches for efficient approximate likelihood and Bayesian computations which have been developed and widely applied in machine learning algorithms and in Big Data analysis, that are not widely known in statistics. The idea of this project is to explore the use of some of these approaches in fitting more traditional, yet complex, biostatistical regression models to large-scale epidemiological data, where exact computations of likelihoods will be infeasible and Monte Carlo sampling techniques reach their limits.

The successful applicant’s time will be divided between MEB, KI, under the supervision of Keith Humphreys and Mark Clements (Senior Lecturers in Biostatistics) and RPL, KTH, under the supervision of Hedvig Kjellström (Professor in Computer Science). You will have access to high-end GPU and CPU computational resources for your research.

Entry requirements
To be able to be employed as a post-doctoral researcher you have to have obtained a doctorate no more than three years before the last date of application (the term can be extended under special circumstances).

The position requires a university degree in statistics/mathematics/computer science and a PhD degree with focus on statistics, biostatistics, medical image analysis, machine learning, or a closely related field. Experience in mixed effects modeling, computational statistics or survival analysis would be ideal.

Application process
An employment application must contain the following documents in English or Swedish:

  1. A complete curriculum vitae, including date of the thesis defence, title of the thesis, previous academic positions, academic title, current position, academic distinctions, and committee work
  2. A complete list of publications
  3. A summary of current work (no more than one page)

The application is to be submitted through the Varbi recruitment system.

Type of employment Temporary position longer than 6 months
Contract type Full time
First day of employment According to agreement
Salary Monthly
Number of positions 1
Working hours 100%
City Stockholm
County Stockholms län
Country Sweden
Reference number 2-1509/2019
  • Keith Humphreys, keith.humphreys@ki.se
  • Lina Werner, HR, lina.werner@ki.se
  • Hedvig Kjellström, hedvig@kth.se
  • Mark Clements, mark.clements@ki.se
Union representative
  • Ulrika Zagai, SACO, ulrika.zagai@ki.se
  • Henry Wölling, SEKO, henry.wolling@ki.se
  • Ann Almqvist, OFR, ann.almqvist@ki.se
Published 14.Mar.2019
Last application date 14.Apr.2019 11:59 PM CET

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