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the standardization of acquisitions, Developing deep learning algorithms to define and learn relevant spectral signatures and link them to clinical phenotypes, Evaluating and validating the platform on representative
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: The objective of this task is to develop a decision-support model to assist in the selection of diagnostic and prognostic algorithms by jointly optimizing energy and computational costs. Two goals are pursued: (i
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, from the quantum processor to the quantum-classical interface and all the way quantum algorithms and applications. Further information on the Department is linked at https://www.science.ku.dk/english
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element tools the lab has developed. The position has the opportunity to develop new tools to enhance the lab’s capabilities in annotating transposable elements (TEs) and repetitive sequences. The Ou Lab is
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Dipartimento di Ingegneria dell'Informazione - Università degli Studi di Padova | Italy | about 1 month ago
immersive and interactive environments. • Definition and modeling of low- and high-level quality-related features. • Development and validation of objective quality assessment metrics for synthesized content
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leading experts working at the intersection of technology, policy, and research. Since its launch, KGI has produced influential research-backed analyses of platform governance, algorithmic design
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within a Research Infrastructure? No Offer Description Group Leader in Quantum Algorithms and Machine Learning (f/m/x) Ref. Number: MAB/06/2026 Location: Warsaw, Poland Salary: 20 750 - 24 250 PLN/month
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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of reinforcement learning and popular modern reinforcement learning algorithms. Students will develop familiarity with both model-based and model-free reinforcement learning algorithms, including Q-learning, Actor
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(LES) results. Key Responsibilities: Develop and refine numerical algorithms for real-time wind field forecasting. Validate forecasting models against high-fidelity LES data and field measurements