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Field
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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the long term. Is Your profile described below? Are you our future colleague? Apply now! Education PhD degree in remote sensing, preferably with a doctoral thesis on RTM inversion or deep learning in remote
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deep learning. You will support the development of an improved forest RTM that can exploit LiDAR full-waveform data along with hyperspectral signatures. You will plan and carry out field campaigns in
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading
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. Candidates with experience in dimension reduction, deep learning, machine learning, modeling neuroimaging data are especially encouraged to apply. Excellent written and communication skills are required
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research
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, training deep learning models on large satellite datasets, and contributing to the integration of forecasting workflows into the IceBox system. The candidate will work closely with an interdisciplinary team
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). Deep learning has been used to perform this mapping from UAV (Batista et al., 2025; Chudasama et al., 2024; Lambert et al., 2025) or satellite imagery (Mattéo et al., 2021), at both very high resolution