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group, established in 2018 at the University of Oslo, has recently expanded to the University of Helsinki. The group specializes in developing computational methods to understand the regulatory mechanisms
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advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning, computational chemistry, or related fields, with
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disposal strategies and sustainable resource management practices. The project also has synergy the ongoing EURATOM EURAD2 RAMPEC work package, and it is expected that some of the methods developed in HiFi
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use a variety of molecular and cell biological methods together with bioinformatic approaches to evaluate the zoonotic potential of newly discovered henipa-like viruses in shrews. As a PhD student, you
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methods to understand the regulatory mechanisms driving cancer heterogeneity and progression by integrating genomic data into genome-wide regulatory networks. Current projects include (i) modeling distal
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support services, e.g. research software engineers. Qualifications An ideal applicant has: A doctoral degree in a suitable field for the work to be carried out. Research experience after completing
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the research group of Professor Klaus Nordhausen in the project “Signal recovery in noisy spatial data”. The research group develops modern and efficient multivariate statistical methods tailored
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the scientists in their general application domain, working together with them to establish practices in the use of AI in their domain. Interaction with various support services, e.g. research software engineers
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theoretical background and a degree in cell and molecular biology (or similar field) and preferably some practical experience in cell culture and/or functional genomics methods. For the Doctoral Researcher
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empirical, experimental and theoretical methods, together with world-class administrative data on individuals and firms. Some of the work is done in collaboration with revenue authorities, also in African