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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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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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the dedicated section here: https://www.iit.it/en/work-at-iit Where to apply Website https://app.ncoreplat.com/jobsharingredirect/777388/generative-ai-machine-learn… Requirements Additional Information STATUS
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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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://chiralnanomat.eu/ we offer two PhD positions: DC9 - Bio-Functionalized Chiral Nanocluster Modelling via Machine-Learning Methods DC10 - Predictive Modelling and Rational Design of Asymmetric Catalysis by Chiral
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The research program involves the study of machine learning
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Job type: Principal Investigator Qualification: PhD Job duration: fixed 5-year term (can be extended for additional 4-years upon positive evaluation) Job hours: full-time Discipline: Life Sciences
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dependable large-scale software systems, integrating expertise in: Software Engineering Machine Learning & MLOps Robotics & Cyber-Physical Systems Cloud & HPC ecosystems Interdisciplinary research. As a
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experience in Artificial Intelligence (AI) and Machine Learning (ML) concepts, algorithms, and frameworks; Hands-on experience with popular ML libraries and tools (e.g., TensorFlow, PyTorch, scikit-learn
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, we will provide young scientists with the opportunity to develop research skills in a stimulating interdisciplinary environment. PhD candidates will acquire specialized technical skills relevant