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Field
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literature. Foundational Algorithms for AGI AGI will not emerge from scaling existing models alone; it requires a new algorithmic foundation for learning, reasoning, and adaptation. This research area is
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Location: Odense, 5230, Denmark [map ] Subject Areas: Pure math with relations to quantum theory or with emphasis on Quantum algorithms, Quantum software and Quantum computing. Appl Deadline: 2025/12/01 11
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outstanding candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex
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employing standard and advanced statistical methods, including machine learning algorithms. The incumbent will also pursue independent research interests, secure extramural funding, and contribute to academic
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Job Purpose To make a leading contribution to the development of innovative muon tracking algorithms for cutting-edge applied nuclear physics research. The successful candidate will be expected
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spectrometry and programming skills. The position is very suitable for a Master's thesis. Furthermore, the working group plans to establish a PhD position in this field in the course of 2026. Although a
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to the discovery process through algorithm design and development of effective computing techniques What We're Looking For Education and Experience Required: Master’s degree in chemistry, engineering, math, physical
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learning algorithms focused on image data, natural language processing, and tabular data Conduct exploratory data analysis and feature engineering for high-dimensional datasets, including image, text, and
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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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& Qualifications needed A first degree in Computer Science, Mathematics, Physics, Electronic/Electrical Engineering, or a related field A PhD (or equivalent experience) in a relevant discipline Solid knowledge