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and meta-analyses, as well as assessing soil health parameters from field experiments. In addition to field experimental investigations, the PhD student will engage with societal stakeholders such as
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explainable AI for large-scale and complex datasets by developing algorithms, pipelines, and tools suitable for critical decision-making contexts. The doctoral student will be based at the Health Technology
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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distributed MIMO systems. Your work assignments The research focus for the advertised position is machine learning for telecommunications. The position is part of the project "Machine learning for sensing in
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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of algorithms, data structures, high-performance computing, machine learning and microbiology. The position at the Department of Molecular Biology at Umeå University is temporary for four years to start
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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healthcare, in most cases, there is only access to information at the patient level, about the patient’s health status and disease development. In this project, we will develop theory, algorithms and methods
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(Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class switching and somatic hypermutation, and identify putative candidate tumor-regulatory
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both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates