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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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understanding of statistics (e.g., hypothesis testing, Bayesian statistics) Good collaborative abilities, independence, and critical thinking. Preferred qualifications In-depth experience with LLM agents
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description Third-cycle subject: Applied and computational mathematics The Department of Mathematics at KTH is announcing a PhD position in Mathematics with a specialization in AI, focusing on Bayesian inverse
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-ecological sustainability and value considerations can be systematically integrated into early-stage design and decision-making. The research objective is to support the acceleration towards sustainable and
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systematically integrated into early-stage design and decision-making. The research objective is to support the acceleration towards sustainable and value-creating lifecycle solutions by enabling more informed
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planning tool which models how autonomous agents make decisions in an uncertain world. This project focuses on designing systems of interaction between agents in order to achieve complex, multi-objective
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model system to validate the findings from the bacterial assays and provide a proof of concept for NP-induced mutational and epigenetic effects in higher organisms. A translational objective is to compare
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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diagnostics, as well as general computer vision applications including autonomously guided vehicles (particularly self-driving cars), image-based localization, structure-from-motion, and object recognition