New SSF project: CNSx3: Transformative Models for Brain Diseases

CNSx3 is a multidisciplinary research center with partners at Uppsala University, Linköping University, Chalmers University of Technology and the University of Gothenburg. The CNSx3 center aims to develop ground-breaking therapies against brain diseases. Three techniques will be used together: organoid cultures in fluid circuits, patient-specific biobanks and advanced computational methodology.

The CNSx3 center is funded by the Swedish Foundation for Strategic Research. 8 research teams will work together to develop new therapies against brain disease using innovative organoid technologies, state-of-the-art molecular biology, and large-scale modeling and computation.

We are now recruiting one doctoral student in statistics and machine learning to join the team. Several doctoral students and postdocs will also be recruited to the partnered research groups at Uppsala, Linköping and Chalmers and form a transdisciplinary research school. You will be joining a large team of researchers, junior and senior, with long experience of working together.

The research conducted by the recruited doctoral student will contribute with machine learning techniques to find new treatments and tailor therapies for individual patients. Specifically, you will work on reinforcement learning methods to propose optimal sequential and combination therapies. The project comprises both methodological and application-oriented research aims. Part of the project will be to develop a digital twin system, augmenting experimental data with synthetic data obtained through models of cancer growth and treatment responses, together with researchers at Chalmers.

To qualify for the position the applicant must have obtained a master’s degree or a 4-year bachelor’s degree in mathematics, applied mathematics, mathematical statistics, or other relevant field or expect to complete such a degree by September 1, 2025. Good presentation and communication skills in oral and written English are required. Experience/expertise in one or several of the following areas may be meritorious: reinforcement learning, statistical machine learning, programming, working with diverse data sources and/or biological applications (e.g., genomic data, image data, biological data bases).

Link to the application system. Only applications submitted through the electronic system will be considered.