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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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be paid to the following experiences: -Experience in sampling and analyses of building materials -Experience in Life Cycle Analysis in construction sector. -Experience in building information modeling
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. Programming gene circuits Modeling and designing synthetic DNA components Construction of Chemical Reaction Networks (CRNs) Simulation and analysis using MATLAB and Visual DSD Robust analysis of various modules
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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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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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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
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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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competence, and a results-oriented and proactive attitude. Meritorious for the position are: Previous research related to CMDs, longitudinal data modelling, human genetics. Assessment Criteria and Selection In