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staff position within a Research Infrastructure? No Offer Description The selected candidate will develop innovative and efficient resource optimization algorithms, suitable for this context, with cost
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algorithms, together with innovative methodological approaches for the clustering/tiling/thinning/sparsening of complex apertures. Where to apply Website https://lavoraconnoi.unitn.it/incarichi-di-ricerca
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FieldEngineeringEducation LevelBachelor Degree or equivalent Skills/Qualifications Strong foundation in software engineering principles, data structures, and algorithms. Experience in designing, developing, testing, and
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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description languages. Develop and optimize EDA workflows for processor and accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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epitaxial interfaces, and can be made free of nuclear spins that would interfere with the electron or hole spins. It is essential to support the development of these advanced quantum technologies with state
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 months ago
Student (f/m/d) in Quantum Algorithms for Fluid and Environmental Flow Modelling. Your tasks # Development and implementation of numerical and algorithmic methods for the simulation of fluid-dynamical
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opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Future Ship and System Design (FSSD) programme aims to develop
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance