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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university
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need to fulfil the following requirements: PhD in one of the following fields: bioinformatics, molecular biology, computer science or related subjects the employer considers of relevance to the position
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Referensnummer IFM-2026-00053 Work assignments This PhD position focuses on methodological and computational development in cryo-electron microscopy (cryo-EM), with emphasis on image reconstruction
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the department’s activities within method development for machine learning-based computational biology. The duties include supervision of PhD students and postdocs, and teaching at basic, advanced and research level
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at KTH in the Biotechnology doctoral program (according to KTH’s regulations). Signing a researcher agreement is a necessary condition to accept this PhD position. More information about Formulation of
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You must have: a PhD in microbiology, infection biology, or closely related relevant fields. Preference will be given to applicants who have completed their PhD or attained equivalent expertise
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral