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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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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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. 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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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