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, biologists, and engineers. You will also have the opportunity to present your research at international conferences, expanding your network and gaining valuable experience in scientific communication
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an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and
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, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
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education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs Flexible working hours
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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six globally networked campuses and international alliances across Asia and Europe. The applicant will be based at the Clayton campus in Melbourne and will have access to international mobility grants
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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to improve structural durability, reduce material consumption, and support the UK’s net-zero goals. Funding notes: The position includes a full scholarship for UK-based PhD candidates and a half-scholarship
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University/NL. The successful candidates commit to actively participate in networking including regular research visits to the partner laboratories. Requirements: university degree in chemistry or physics and
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for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature, 2024, 631, 549–555). This work demonstrated that self