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of Veterinary Medicine includes over 600 students (including a Doctor of Veterinary Medicine (DVM), Master of Professional Studies (MPS), Doctor of Philosophy (PhD), and Master of Public Health (MPH) degrees
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] Subject Areas: Physics / Hard Condensed Matter Theory , Machine Learning , Material Science , Physics , Quantum Information Science , Soft Condensed Matter Theory , theoretical condensed matter physics
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d) excellent written, oral communication skills e) strong data analysis skills. Ideal applicants will also have experience with some combination of: a) Machine learning e) code optimization and
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. theses at the interface between structural engineering and machine learning. You will disseminate your research through peer-review publications and participation in international conferences. You will
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, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by developing theories, principles, tools and methods
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
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and apply methods for analyzing genomic variations, e.g. SNV, SNP, SV, CNV, methylation, and gene expression Develop tools using machine learning and AI Prepare high-impact manuscripts for publication
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, or machine learning experts to create predictive virtual 3D mammalian embryos for human health, especially congenital heart diseases. We welcome applicants with expertise in genomics, developmental biology
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extraction) that can be miniaturized and integrated into portable devices. Perform SERS measurements and data analysis of SERS data (e.g., using machine learning). Develop, test and apply new fiber-based SERS
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques