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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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and motivated PhD student to join an interdisciplinary project that combines computational biology, spatial transcriptomics, and tumor modeling to understand how the aggressive brain tumor glioblastoma
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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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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environmental factors. Findings will be further explored through bioinformatic methods. Other techniques may include machine learning and mathematical modeling. Additional tasks within the research group may also
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application! At the Department of Electrical Engineering, Division of Automatic Control, we are now looking for a PhD student, to be admitted to the WASP graduate school. Your work assignments The research area
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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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application! We are now looking for a PhD student in Automatic Control, at the Department of Electrical Engineering (ISY). Your work assignments The research area of this position is complex networks and
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research activities in stormwater management. The research is both theoretical and experimental with elements of computational technology and mathematical modelling and is based on close collaboration with
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of mathematical modeling and data analysis. Experience of programming languages and tools commonly used in biophysical or agricultural modeling (e.g., Python and R). Familiarity with food system resilience