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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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the following bases of assessment: Documented subject knowledge of relevance to the area of research Analytical skill Other documented knowledge or experience that may be relevant to doctoral studies in
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equivalent knowledge in Sweden or elsewhere. Specific entry requirements are described in the general syllabus for doctoral studies in the field of chemistry . Selection The selection among the eligible
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covers research that fundamentally transforms our knowledge about how cells function by peering into their molecular components in time and space, from single molecules to native tissue environments. A PhD
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, working in data-driven cell and molecular biology. Data-driven cell and molecular biology covers research that fundamentally transforms our knowledge about how cells function by peering into their molecular
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of the applicants will be evaluated on an overall basis. Karolinska Institutet uses the following bases of assessment: – Documented subject knowledge of relevance to the area of research – Analytical skill – Other
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treatment targets. Data driven cell and molecular biology covers research that fundamentally transforms our knowledge about how cells function by peering into their molecular components in time and space
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on an overall basis. Karolinska Institutet uses the following bases of assessment: – Documented subject knowledge of relevance to the area of research – Analytical skill – Other documented knowledge or experience
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acquired substantially equivalent knowledge in some other way. We are looking for a candidate who has a strong interest in data-driven research in infection biology. Previous practical experience in machine
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KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science This project involves generative modeling