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Vacancies PhD Position on Neural Engineering/Computational Neuroscience/Biomedical Engineering Key takeaways We are seeking a highly motivated PhD candidate to join a multidisciplinary team working
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Starting date: 01.12.2025
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? We invite applications for a PhD position, focusing on the design and implementation of Spiking Neural Networks (SNNs) using CMOS technology. Project Overview This PhD position is part of a
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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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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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-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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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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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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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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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