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learning). The project will be carried out in a collaboration between Linköping University (main supervisor: Anders Eklund) and Lund University (co-supervisor: Mikael Nilsson) through an ELLIIT collaborative
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. The student will collaborate closely with the PI and her research group at Umeå University to investigate the proposed learning approach. The PhD student is expected to produce research outputs relevant
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learn experimental and computational approaches to tackle fundamental biological questions with medical relevance using innovative system-wide techniques. You will work on an exciting multidisciplinary
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collaborations with other divisions at Linköping University, including the Division of Statistics and Machine Learning (e.g., Fredrik Lindsten and Zheng Zhao ), as well as internationally (China, Finland
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at the Department of Mathematics . Furthermore, our group maintains active collaborations with other divisions at Linköping University, including the Division of Statistics and Machine Learning (e.g
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, TP63) using molecular biology methods. The position involves collaboration with international researchers within an EU project framework as well as outside the EU, including short research stays in
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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies