32 parallel-and-distributed-computing-phd-"Multiple" positions at AALTO UNIVERSITY in United Kingdom
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Join Aalto University to drive innovation in science, technology, and more. The School of Electrical Engineering invites applications for an ASSISTANT OR ASSOCIATE PROFESSOR IN COMPUTER ENGINEERING
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ambitions We are seeking a candidate with: Educational background PhD in Architecture, Landscape Architecture, Computational Design, Computer Science, Urbanism, or a closely related field. Specialization in
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invites applications to Doctoral Researcher positions in Computational Physics The positions are hosted in the Surfaces and Interfaces at the Nanoscale(SIN) group at Aalto, led by Prof. Adam Foster. We
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development. For example, receiving mentoring, training on large-scale computing, or possibilities of mentoring PhD/MSc students and teaching, if that is what you are looking for Opportunity to work in Finland
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assessment in architecture and construction? Do you want to work for turning down the emissions and overuse of materials in the built environment? We are now looking for a doctoral candidate for a PhD position
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, receiving mentoring, training on large-scale computing, or possibilities of mentoring PhD/MSc students and teaching, if that is what you are looking for Opportunity to work in Finland, which is a safe
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is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions. The concept has lately gained increasing interest from
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. At the same time, 20-50% of industrial energy is ultimately lost as waste heat. With the rising demand for computing power, data centers are producing increasing amounts of low-grade waste heat. Likewise, as
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Learning Your role and goals Trustworthy & Adversarial Computing Lab (https://taclab.aalto.fi ) led by Sebastian Szyller is looking for a doctoral researcher (PhD student) to pursue a degree in trustworthy
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees