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will have the chance to use cutting-edge techniques, including super-resolution microscopy (PALM, Smdm), single-particle tracking, machine learning, and latest state-of-the-art biochemical analysis tools
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on studies of visual perception and decision-making. Research methods include computational modelling, brain imaging (fMRI), machine learning, behavioural methods, and other techniques. Virtually everything we
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economicssystems/ operations management, econometrics, machine learning, extensive data analytics and qualitative research methods. The student will collaborate closely with advisors to identify, develop, and carry
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that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group
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the power of data and machine learning! Job Description We are seeking a highly motivated PhD candidate to join our research team focused on Collaborative Metadata Management for Large Data Repositories
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) to thoroughly evaluate the security of post-quantum protected implementations against deep-learning-based side-channel analysis. We invite applications for a PhD position at the Computer Architecture
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some academic research experience post-Master level. Demonstrable affinity with archival sources. Strong skills in GIS-based research, additional experience with computer vision and machine learning is a
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neuroimaging (fMRI, MEG, iEEG) and behavioral data. This PhD position is part of the Natural Auditory Scenes in Humans and Machines (NASCE) project, funded by the ERC Synergy Grant, and hosted at Maastricht
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-heuristic search algorithms such as Quantum Annealing (QA). Your contributions will be pivotal in advancing the field of DSE in the computer system domain. This PhD position offers the chance to not only push
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Institute for Brain, Cognition and Behaviour. You will work on studies of visual perception and decision-making. Research methods include computational modelling, brain imaging (fMRI), machine learning