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microtumor models. This work addresses a critical knowledge gap in cancer immunobiology and supports the development of more accurate disease models. Duties The main duty for a doctoral student is to devote
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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methods in applied mathematics and computational modeling, this specific project aims to uncover new insights into how blood cells form in both healthy and disease states. A key objective is to model
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from satellite, aircraft or ground sensors to understand, model and retrieve parameters relevant to the processes driving our atmosphere, biosphere, hydrosphere and cryosphere. About the research project
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or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health
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Professor in Theoretical Computer Science at LiU. The research for the advertised position will be within the WASP PhD project ”Model-Based Attention for Scalable AI Planning ”, where we will integrate
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rate, and virtually nothing is known about a putative connection between these mutation rates. Using several Drosophila melanogaster model systems, in combination with quantitative genetics, experimental
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methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is
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’. The department undertakes fundamental research on model organisms, agricultural crops, forest trees and bioenergy crops. Our main areas of research comprise the interaction of plants with microorganisms and other
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Associate Professor Åsa Johansson at Uppsala University, Department of Immunology, Genetics and Pathology. The group focuses on identifying risk factors for common diseases and developing models for risk