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Research Framework Programme? Horizon 2020 Reference Number 101226717 Is the Job related to staff position within a Research Infrastructure? No Offer Description DC2: Advanced approaches to understanding
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-Curie Actions Doctoral Network funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101226717. Work Location(s) Number of offers available1Company
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Description Are you fascinated by the incredible capabilities of neural networks? Are you interested in applying theoretical methods to understand computational efficiency in neural systems? If so, come and
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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to classical artificial neural networks [8]. Both characteristics – reduction of data and sparse computation – make event-based computing a promising framework for designing and building energy-efficient
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Application deadline: 05.10.2025 Tasks To perform an independent experimental or computational PhD project in the fields of neuroscience, biochemistry, ophthalmology, and / or datascience. Profile Completed
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | about 7 hours ago
institute of the Max Planck Society and is dedicated to the study of human cognitive abilities and brain processes as well as the development of neuroimaging methods. The Brain Networks Research and
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manner; Actively participate in research meetings and all training and network activities of the ELEVATE consortium. Enroll in a doctoral training program at the Arenberg Doctoral School, and adhere
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through the EU Research Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 0 Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you
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of Chemistry. This position is intended to begin January 1, 2026. The aim of this project is to develop and apply computational methods for statistical learning on chemical reaction network (CRNs) structures