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knowledge graph models of such transformations that are self-describing when applied to existing map repositories, and can be scaled up to large data repositories using state-of-the-art AI methods. In
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tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its programs in the liberal arts, medicine, business and law
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in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function verification and formal
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., omics or clinical data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, with a focus on omics data
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contribute to teaching. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development
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science or similar. Experience with Fortran or other compiled software languages Experience with high performance numerical methods and parallelization Experience with writing and publishing scientific articles
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | 15 days ago
: · PhD in Mathematical Biology, Ecological Modeling, Economics, Fisheries Science, or a related field. · Strong background in ecological and/or economic modeling, population dynamics, and quantitative
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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: Extensive experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and
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and proposal preparation. Required qualifications: As a formal qualification, you must hold a PhD degree (or equivalent) in computer science, computer engineering, networking, or related fields relevant