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multiplex analysis. We will assist the computer scientists to apply artificial intelligence Machine Deep Learning models using the omics data of mitophagy to predict risk of cancer and metastasis and design
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combined formal verification and AI techniques. This involves creating advanced techniques of modeling complex autonomous systems that work in unpredictable environments, capturing unpredictability, and
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: • Designing and conducting multi-omics analyses (including genomics, transcriptomics, proteomics, metabolomics). • Constructing new AI-driven multi-omics models • Supporting occasional teaching and supervision
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the context of this position, we will focus on understanding the functional basis of interactions between hosts and their co-infecting viruses in multipartite model systems. We also will develop other host
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modeling using CFD (Computational Fluid Dynamics) tools. The PhD candidate will also work on the development of simplified calculation models and the formulation of guidelines for designing combustible
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Science, Natural Science and Geography. The teaching includes responsibility for a bachelor's programme, a master's programme, single subject courses, and courses in the Primary School Teacher Programme
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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bioinformatics and evolutionary models The project will involve limited amount of experimental testing of computation models. A willingness to engage in this work is required. Experience in practical work
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and computational statistics. More information can be found on the department's website https