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optimality and allows influence to be assessed at various stages of optimization. 2. Addressing Computational Challenges** Applying influence functions to deep neural networks presents **computational
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meetings and all training and network activities of the ELEVATE consortium. ● enroll in a doctoral training program at the Arenberg Doctoral School, and adhere to the doctoral school's coursework
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The University of Exeter’s Department of Computer Science is inviting applications for a PhD studentship funded by Faculty of Environmental Science. For eligible students, the studentship will cover
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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knowledge acquisition (machine learning), the complexity of learning algorithms, pattern recognition with artificial neural networks, computational geometry and information processing in biological neural
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for three years from March 1st , 2026. Project description The advertised PhD position forms part of a competence network supported by the EU Interreg-6a funding scheme between Germany and Denmark. The vision
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run by experts in their field. In addition you will be part of the ENSAFE project, including stays with our project partners in Ålborg and England, and with access to the wider consortium network and
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initiatives Participating and presenting at international conferences Generate new scientific knowledge Salary The Doctoral Network “SPACER” is financed by the European Union under the framework of the program
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Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search