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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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intelligence, or a related discipline. You have a strong interest in performing foundational algorithmics and machine learning research. You have mature mathematical knowledge and programming skills (e.g
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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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demanding applications as in environmental and exposure monitoring, and the green hydrogen industry? If so, then you have a part to play as a PhD candidate in our research team. Put your ideas to the test at
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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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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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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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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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calculated using our Software Energy Lab, which has multiple test machines with GPUs and, in the future, AI accelerators. Development teams currently lack guidance on how to create sustainable systems. You