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and bottlenecks, as well as communication requirements. Develop and evaluate quantum computing hardware and software architectures that benefit from the idea of modular, interconnected and possibly
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: • Scientific achievements in engineering and technical sciences • Additional qualifications (in line with the education held) confirmed by a certificate Where to apply Website https://up-sanok.edu.pl/inne/oferty
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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21 Aug 2026 Job Information Organisation/Company Poznan University of Physical Education, POLAND Research Field Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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Security processes; knowledge of Romanian at a native level or demonstration of an advanced level (level C1/C2). fulfilling the standards provided in the Competition Methodology for filling vacant teaching
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Operational Technology Apply for this job See advertisement This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in
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of coronary artery disease and supporting clinical decision-making during catheterization procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop
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) solutions, multimodal signal processing, and digital biomarkers for accessible sleep monitoring in adult and pediatric populations with different health conditions. The aim of the project is to develop new
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and simulation techniques are necessary to develop Urban Digital Twins able to manage this complexity and produce reliable predictions. The candidate will be requested to contribute to this research
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of the proposed PhD are: (i) To investigate novel neural scene representation methods and their complementary with respect to conventional geometric methods (ii) To develop methods to decompose raw 3D scenes