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Are you interested in making the processes for building machine learning models more transparent and explainable? Would you like to work in interdisciplinary teams to uncover the needs of clinical
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advanced motion planning algorithms with machine learning techniques, such as reinforcement learning, imitation learning, and task generalization. You will focus on designing intelligent robotic systems
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(+/–) applied to the electrodes can be optimised in a machine-learning approach to optimise the gel morphology and the embedded stimuli-transduction pathways towards the targeted mechanical behaviour. Departing
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to the forefront of studying animal behaviour using machine learning, with a particular focus on distress monitoring. Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in
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. Research methods include computational modelling, brain imaging (fMRI), machine learning, behavioural methods, and other techniques. Virtually everything we sense, think and do is uncertain. For instance
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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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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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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
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machine learning or computational statistics or are eager to learn. Experience or affinity with constructing basic electrical circuits is a plus. You flourish in a team-centered, multicultural and
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or an interest in automation and programming. You know how to take the lead in your project, but you are also happy to support others in their work. Ideally, you have experience with machine learning