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/ ) – an European network of excellence in AI, Machine Learning (ML) and Computer Vision (CV), of which Vittorio is a Fellow member. For this particular position, the focus is on investigating AI approaches
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(Python or C/C++) with experience in systems engineering and software development. Solid knowledge of both basic and modern methods in machine learning, NLP and computer vision, including supervised and
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and image generation based on deep learning. The aim is to study techniques for handling multimodal data by integrating visual information (2D and 3D) with textual or tabular metadata. This integration
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Development of innovative experimental model systems for mechanistic investigation and translational validation of microbiome-mediated processes Advanced AI and machine learning frameworks for integrative multi
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maintain end-to-end EO data processing pipelines, from sensor calibration to the extraction of geophysical variables. Implement machine learning and image processing techniques to fuse optical and SAR data
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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Research Infrastructure? No Offer Description The requested figure will be responsible for developing and implementing both machine-learning methods for analysing images and audio files in Python, as
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Description REALISE - Bridging Igneous Petrology and Machine Learning for Science and Society About the REALISE Doctoral Network REALISE will train 15 Doctoral Candidates at the interface of igneous petrology
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fluorescence microscopy (SMLM, SIM), integrating physical-mathematical models, machine learning, and compressed sensing for accurate and efficient reconstructions. Applicants must submit a project implementing
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experience with EB-PBF systems is considered an asset) Python programming Statistical learning / machine learning / machine vision / Artificial Intelligence methods Image and signal processing (familiarity