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- Autonomous University of Madrid (Universidad Autónoma de Madrid)
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are: - Management and preprocessing of databases containing data and signals, i.e., audio, images, biomedical and genetic. - Design, implementation, and testing of deep learning and AI algorithms for processing
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of databases containing data and signals, i.e., audio, images, and biological. Design, implementation, and testing of deep learning and AI algorithms for processing audio, image and biological signals and data
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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-based principles. The successful candidate will train, benchmark, and develop deep learning architectures. They will work in high-performance computing environments and apply expertise in statistical
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/MS workflows, spectral library searches and data curation. Search the scientific literature for suitable protocols and systematically test promising approaches to improve sample preparation. Learn
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: mathematics (algebra, geometry, probability theory, optimization theory), statistics, theoretical physics, theory of computation. Cutting-edge Disciplines: deep learning theory, generative models, graph neural
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with deep learning to design van der Waals heterostructures with optimised spin-orbit torque (SOT) efficiency for ultra-low-power memory and computing. Its two pillars are AUTOMATA, an automatic material
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School of Quantum at the University of Chinese Academy of Sciences (UCAS) | Spain | about 15 hours ago
Ph.D. or equivalent degree and have demonstrated ability to conduct outstanding research. Successful candidates are expected to carry out independent and innovative research, be able to teach both
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mental disorders across development. We develop and apply mechanistic models (e.g. reinforcement learning, normative modelling), generative approaches to augment neuroimaging data, and digital twin brain
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, engineering or physics. Knowledge: Computational programming, machine learning, quantum transprot, device simulation. Professional Experience: use of device simulation codes applied to 2D materials. Personal