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techniques based on machine learning with deep neural networks (DeepSets) using GPUs. Data and tools from the LHCb experiment will be utilized. Additionally, contributions to the data analysis of LHCb
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Localization Microscopy (SMLM), particle tracking and cryo-electron tomography (cryo-ET). The measurements will be integrated using machine learning and data modelling. Our lab combines cell engineering and
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communication skills (verbal and written). • Solid understanding of Pattern Recognition, Machine Learning, and Deep Learning. • Good programming skills. • Proactive, curiosity-driven, and
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, and refining methods to transform insecure code into secure code, possibly employing machine learning or AI. Candidates may delve into binary software or source code leveraging compiler toolchains
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: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices, ACM MobiHoc 2023 [2] A. Collet, A. Bazco Nogueras, A. Banchs, M. Fiore, AutoManager: a Meta-Learning Model for Network
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training, often but not always local, with order of 5 ECTs on specialized topics, close to a given DC personal project, to allow him/her to acquire a deep understanding of her/his subject. Second, the
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level: C1-C2. Drive licence. Desirable qualifications and skills: Deep Learning experience (Keras, TensorFlow, Caffe...). Big Data experience (Hadoop, Spark). Computer Vision experience (OpenCV). We look
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various social media channels. Publications are very important during the project duration. The researcher will be provided with supervision in a cross-sectorial team with deep expertise on satellite-based