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work on machine learning, AI security, and real-time embedded computing, with a strong emphasis on the AI and trustworthiness side of the problem. Tasks The PhD student will contribute to research
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Design. In addition, the project involves close collaboration with the Department of Architecture, Design, and Media Technology and involves one other PhD candidate and a postdoctoral researcher. The
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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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analysis, causal inference with machine learning, and deep learning for various health-related domains. The Global Pathogen Analysis Platform (GPAP) is a new international initiative to strengthen global
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significant contributions to fundamental machine learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics
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• Participate in the department’s research environment • Complete a PhD training programme • Teach at one or more of the department programmes Your main task as a PhD student will be to develop and complete a PhD
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predict interactions in dynamic ecological networks. Our lab is looking for candidates for the following stipend: Learning the Structure and Dynamics of Complex Networks We seek a PhD candidate to work
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seek a PhD candidate to work on representation learning methods on graphs for modeling static and temporal networks, with applications to ecological systems and beyond. The project will focus
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methodological development and application of bioinformatics, biostatistics, machine learning, and data management within clinical research. CLINDA is interdisciplinary and employs biostatisticians
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, cryo-electron microscopy (cryo-EM), cryo-EM-based polyclonal serology (cryo-EMPEM), molecular dynamics simulations, machine learning, and structural biology to define epitopes and engineer improved