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Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
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: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
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regulators of disease onset and progression. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating
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. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating multimodal data with clinical information
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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of the Saragovi Lab is to develop and apply a combined computational, Artificial Intelligence (AI) and high throughput experimental approach to systematically infer protein-semiconductor hierarchies materials and
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, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by
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fungal communities may be changing through time, and infer the functional significance of this, in terms of changes in organic matter composition, and C accumulation rates. About the position The postdoc
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Network Inference https://www.umu.se/en/ucmr/ec-postdoc-programme/ncrna_net-development-of-a-novel-approach-to-lncrna-mrna-regulatory-network-inference/ 7. Virome–vector competence shifts: How mosquito