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work — and how to design better ones. Why apply for this PhD? Work on the next-generation AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics
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contexts, or a strong motivation to explore how different groups of learners appropriate AI-based tools. A strong technical background, ideally in computer science, software engineering, human-computer
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
, collaboration with multidisciplinary teams, and the development of projects at different scales, including detailed design. Proven experience in the use of the following software: Revit, AutoCAD, Adobe InDesign
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architectures for next-generation wireless communication. Design ultra-low-jitter, low-spur PLLs and validate them on real silicon. Job description Why this position is important Future wireless communication
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Your Job: As a doctoral researcher in the group of Dr. Susanne Kunkel at the institute Neuromorphic Software Ecosystems (PGI-15), you will contribute to the advancement of simulation technology for
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receiver and ADC architectures for next-generation wireless communication. Design time-domain receivers and ADCs and validate them on real silicon. Job description Why this position is important Future
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modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data. The development of these methods is motivated by a concrete and important application: inferring gas
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administrative agents, about 150 PhD students. The lab is very open to international collaborations; more than 100 foreigner scientists coming from 20 different countries are currently working
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areas, which will be used to train and assess boundary layer neural network models. The student will develop and evaluate suitable ML architectures, analysing the trade-offs between different modelling