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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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). 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
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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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appearing in the ACL Anthology. Substantial experience working with leading deep learning frameworks for training and running language models along with using language models via APIs. Relevant experience
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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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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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programming and instrument control using Matlab, Python, Labview etc Machine / deep learning expertise Strong analytical skills and ability to work in a multidisciplinary team Excellent communication and
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Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and
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@emploi.beetween.com Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Expected skills: Hold a Ph.D. in Deep Learning, Statistics, or a related field. Solid experience in
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view