The position is shared between Prof. Thomas Schön at the Department of Information Technology and Prof. Johan Elf at the Department of Cell and Molecular Biology (ICM). The collaboration creates prerequisites to develop new deep learning-based methods for studying high-impact biological processes. There are also excellent opportunities to interact with leading research groups in Sweden through our connections in the WASP network (https://wasp-sweden.org/) and within Europe through our connections in the ELLIS network (https://ellis.eu/).

The Department of Information Technology has a prominent position in machine learning research. Thomas Schön develops both theory and applied tools for computer-driven learning, reasoning, and decision making to improve both people’s and machines’ understanding of real-world complexity. Probabilistic models are central in the research, allowing a systematic representation and managing of the uncertainty inherent in most data. More info: http://user.it.uu.se/~thosc112/index.html. Johan Elf’s research group at the program for molecular systems biology works interdisciplinary with large-scale genetic engineering and sensitive measurement methods to investigate life at the molecular level. More info: https://elflab.icm.uu.se/

Duties: You will be responsible for the development of flexible models (such as for example deep neural networks or Gaussian processes) for the analysis of measurement sequences from several instances of a study object that change over time. This includes significant elements of basic research in Machine Learning and in collaboration with researchers in cell biology (at ICM), you will use these models to derive a 4D structure of the bacterial chromosome from image sequences. Such a structure would be imperative for the cell biology research field and answer many outstanding questions regarding the impact of the chromosome structure on gene regulation and microbial physiology and pathology.

This is a full-time research position. You are expected to run your project independently and you have the freedom to develop your ideas within the overall framework of the project. We offer a stimulating interdisciplinary environment where you work closely with experts in deep learning, statistics, signal processing, microbiology, microfluidics, and image analysis. As a postdoctoral fellow at the Department of Information Technology, you will benefit from the strong machine learning research community at Uppsala University. At the same time, the collaboration with ICM provides a good insight into how data is generated and the possibility of influencing the availability of training data.

Requirements: PhD degree in machine learning, signal processing, computational statistics, or within a closely related area or a foreign degree equivalent to a PhD degree in machine learning, signal processing, computational statistics, or within a closely related area. The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are primarily considered for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

To be eligible for the position, you must have good knowledge in machine learning (especially deep learning or modelling of sequences/dynamic systems), as well as a self-motivated and creative personality and a great interest in basic research. Since the project requires collaboration with researchers both within and outside the group, great social skills are required. You should be able to communicate freely in English.

Please submit your application by 9 June 2022, UFV-PA ²⁰²²⁄₁₆₀₀ here