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Postdoctoral Researcher in Quantum Computing Middleware and Applications Employer UNIVERSITY OF HELSINKI Location Helsinki, Finland Salary 4000-4400 €/month Closing date 21 Oct 2026 View more
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leading unit in Finland in its area and responsible for the teaching and research in computer science at the University of Helsinki. The main research fields at the department are artificial intelligence
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Australian Institute for Bioengineering and Nanotechnology (AIBN) Full-time (100%), fixed-term position for up to 2 years Base salary will be in the range $85,373 to $113,660 + 17% super (Academic
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continuous-variable cluster states on chips or in fiber, with relevance to both MBGBS and universal measurement-based quantum computing. Explore methods for generating, injecting, detecting and using non
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the crack tip You will use image-based measurement techniques, such as digital image correlation (DIC), particle tracking, and other quantitative methods, to quantify displacement and strain fields
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 10 hours ago
conditions and UUV operations. The position requires a high level of independent research skill and technical expertise in physical oceanography, numerical modeling, and scientific computing. The postdoctoral
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conditions and UUV operations. The position requires a high level of independent research skill and technical expertise in physical oceanography, numerical modeling, and scientific computing. The postdoctoral
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project at King’s on quantum algorithms and numerical methods for many-body dynamics beyond the reach of standard classical simulation. About the role We have a high preference for a candidates who is
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Physics, specializing in neutronics or nuclear engineering Computing skills: Proficiency in C++ (as the core computational kernels are written in this language) Applied mathematics skills: Numerical
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cyclones, the Madden–Julian Oscillation, and atmospheric rivers. We combine theory, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics