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Field
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a
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DTU Management invites applications for a 3-year PhD Scholarship associated with the project “Brains in Motion: Explaining How Humans Learn and Adapt to Navigate Urban Environments Using Artificial
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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advanced machine learning techniques Optical design and miniaturization of a novel spectroscopic sensor based on freeform optics, optimized for agrifood applications For this function, our Brussels
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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multimodal foundation architectures. Full-time, TV-L E13, 100%, fixed-term for 3+ years. Position PhD Student (100%) Remuneration TV-L E13 (Fully Funded, fixed-term for 3+ years) Field Representation Learning
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries