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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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Postdoc in Generative Machine Learning for Biomedical Data | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 21.02.2026 Join a place where
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the next generation of secure agentic AI systems through cutting-edge research in adversarial machine learning and formal verification. The Role As a research scientist, you will contribute to frontier AI
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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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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communication skills in English ADDITIONAL SKILLS Further experimental skills Strong problem-solving attitude High motivation to learn Spirit of innovation and creativity Good at time and priority management
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, stochastic differential equations, computational methods in fluid mechanics and turbulent flows, high-performance computing, machine learning methods in computational problems. GSSI is a world-renowned
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intelligent robots. Developing adaptive learning and deployment frameworks enabling secure, trustworthy, and robust operation of AI models embedded in CPS. Conducting empirical and experimental studies at scale
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communication skills; Strong sense of curiosity and willingness to learn and develop; Teamwork approach, good communication and relational skills; Strong orientation to results, determination, flexibility and