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candidates with an outstanding research record in deep learning, in particular in one or several of the following areas: modeling and architecture development, domain adaptation & continual learning, agentic
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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field, have a strong record of research productivity (e.g., publications and software) in mass spectrometry-based proteomics, and possess substantial experience developing algorithms and pipelines
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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data preprocessing, algorithm development, and optimization techniques. Excellent communication skills in English. Prior experience with environmental or photocatalytic systems is a plus. A fully funded
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design using brute force methods by using non-discriminate parameter variations and recently innovative approaches using artificial neural networks (ANNs) are also developed for the prediction
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their thesis research. The candidate will work closely with Prof. Olivares-Mendez and Dr. Carol Martinez, the members of the Space Robotics (SpaceR) research group (www.spacer.lu ) and Redwire Space Luxembourg
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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control and reinforcement learning supported by an edge-cloud-based wireless communication environment. The doctoral student will work on data-driven theory and method development in simulation environments