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extremes under different air pollution along a climatic gradient in Europe. Moreover, we will use statistical (e.g., random forest, generalised additive models) and process-based (e.g., SPA) models
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and communication strategies. This ambitious project invites passionate PhD candidates to contribute to pioneering studies that merge theoretical insights with practical applications, marking a
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of fundamental and disease-focused areas. To advance these efforts, the Platt group is recruiting a full-time (100%) PhD Student to develop and apply in vivo functional genomic methods. Project
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science, statistics, applied mathematics, or related fields Proficiency in developing and deploying machine learning models (e.g., using Python, R) Experience in data wrangling and feature engineering
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of fundamental and disease-focused areas. To advance these efforts, the Platt group is recruiting a full-time (100%) PhD Student to develop and apply in vivo functional genomic methods. Project
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range of expertise, from software engineering and biomedical data management to statistical and bioinformatics services. Embedded in this multi-disciplinary facility, the screening and lab automation
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ETH Zurich studies water and climate extremes in mountain regions under global change. We have strong expertise in hydrology, climate sciences, extreme value analyses, statistical modelling, and climate
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experience in Machine Learning with a PhD degree from a university in Computer Science, or related fields, with a proven track record in statistical machine learning, deep learning, and graphical modelling
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skills as artificial intelligence and machine learning, statistics and modeling. Job description We search a 50-60% coordinator to further develop the curriculum of the Doctoral Program in Plant Sciences
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and statistical methods to detect and characterize trait-associated sequence variation from short and long-read sequencing data. We are looking for a motivated researcher who is eager to work on cutting