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; T2: State-of-the-art review and model testing; T3: Development of algorithms for information extraction; T4: Algorithm testing and validation; T5: Preparation of reports, presentations, and other
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to process the quantity of experiments conducted with one or more fish swimming simultaneously, on the one hand; and on the other hand, to implement a data fusion algorithm to improve the overall precision
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prediction under extreme conditions, resilient cybersecurity solutions, and self-healing algorithms. Candidates should demonstrate not only extensive experience in deep learning, reinforcement learning, and
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have: experience with spiking neural networks and neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and
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and prior experience in the design and implementation of mathematical optimization algorithms. (18 points) Knowledge and prior experience with statistical prediction models. (10 points) Knowledge and
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 3 months ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment
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scenarios and systematic literature review on algorithmic transparency and historical benchmarks in AI for healthcare. Selection and Evaluation of Methodologies: Identification and classification of XAI
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systems * knowledge of modern artificial intelligence algorithms * practical experience in implementing IT services using artificial intelligence technologies * experience (minimum 5 years) in teaching
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
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applications for a Research Assistant Professor position. We seek candidates whose research examines how increasingly agentic AI (e.g., large language models, algorithmic systems) shape users’ psychological and