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motivated experimentalists who are deeply excited about working with nonhuman primates and connecting those results to computational models of the brain. The ideal candidates will enjoy hands-on neural
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-responsive behaviour tasks using tasks that mimic biological learning principles and neuromodulation. Artificial neural networks with biologically-inspired dynamics will be trained and progressively pruned
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advanced AI/ML methods to neural and behavioural data to uncover the computational foundations of decision-making in humans and animals. Designing novel AI algorithms inspired by brain function, to improve
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to provide PhD students with an in depth understanding of the fundamental theories and learning methods, as well as advanced issues of neural networks and deep learning. Students will gain an in depth
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better ways to design neuromorphic or brain-inspired computing systems. These mechanisms must be energy-efficient, reconfigurable, and secure, especially for edge devices. This PhD project focuses
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dynamical system modeling tools using specialized software packages to elucidate neural computations involved in emotion. Prepare and contribute to grant applications and conference presentations
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. The position gives excellent opportunities in publishing in high-quality forums. We expect the applicants to have a PhD degree (or close to completing one) in computer science, mathematics or statistics, with
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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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/Qualifications Required qualifications: B.Sc. in life sciences. M.Sc. in life sciences. Enrolled as a student in a PhD Program in life sciences Oral & written skills in English language. Desired qualifications
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employment conditions, and strong support for personal and professional development. The PhD student will be enrolled in the ETH Zürich / University of Zürich doctoral program , depending on academic