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(PhD or MSc) Our goal: Build a future-proof data platform that accelerates plant breeding research. Your colleagues: An interdisciplinary team of data engineers, researchers, and domain experts within
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distinguer, d'un point de vue statistique, plusieurs processus ponctuels marqués et à identifier les statistiques discriminantes les plus pertinentes. Ces statistiques sont construites à partir de graphes
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; Secure independent and collaborative funding from national (e.g., NWO) and European (e.g., ERC, Horizon Europe) sources, as well as industry partnerships; Supervise and mentor PhD candidates, postdocs, and
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. The person appointed will have an undergraduate degree in Computing, Computer Science, Mathematics or a relevant area. A PhD in a relevant field is essential. You will have experience teaching a broad range of
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. You will accurately record and compile data, conduct robust statistical analyses, interpret results, and summarize findings in tables, graphs, reports, and publishable formats. You will work both
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language, most people also speak English. YOUR TASKS The Marchal lab (IDLab/IMEC Ghent University,https://idlab.ugent.be/people/802000961346) has an open PhD position within the Marie Sklodowska-Curie
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of the educational programs within the School of Medicine (SOM). This includes, but is not limited to, over 4,000 students and learners enrolled in SOM degree seeking programs (i.e.: MD, PhD, master's and dual-degree
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some overlapping measures in the individual data sets and through the use of advanced analytic tools including machine learning and graph theoretics, one can discover multiple developmental pathways in
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statistical analyses, interpret results, and summarize findings in tables, graphs, reports, and publishable formats. You will work both independently and collaboratively as part of the BPP and VSP programs
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the environmental impact of cloud infrastructures, making this PhD topic highly relevant to national and global sustainability goals. Scientific Objectives This thesis aims to develop novel methods for deploying AI