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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and
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approaches beyond standard deep learning methods. Demonstrated experience in academic research (e. g. through publications, conference contributions or comparable scientific achievements). Very good
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research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships
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University’s distinctive applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world settings. Through strategic partnerships forged by our academic staff with
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communication skills; ability to work effectively in an international environment. • Demonstrated ability and motivation to learn new techniques and procedures. • Experience with control and data acquisition
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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. Desired additional skills: • Independence and decision-making ability; • Openness to new concepts, ease of learning. Job description: • Conducting experimental research in the laboratory
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent