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focused on advancing computational methods for synchrotron, free-electron laser (FEL), and related scientific challenges. SciQC combines expertise in mathematics, numerical algorithms, high-performance
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for technical-scientific domains that do not correspond exactly to this call. Job Description FBK is looking for candidates to fill one position in Deep Learning methods applied to projects within the DSIP unit
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the Distributed AI for dependable cyberSecuritY (DAISY) Unit, of FBK-CS, which focuses on developing novel AI-driven methods for cyber threat detection in distributed computing environments, with a particular
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linear algebra, numerical methods for PDEs and dynamical systems, stochastic methods in statistical mechanics, hydrodynamic limits, interacting many-body systems, quantum macroscopic evolution equations
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to ongoing and related projects as needed; Contributing to the Centre’s training program by teaching workshops or tutorials in machine learning, generative models, or data science methods; Engaging with public
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zebrafish larvae with advanced optical methods. The position will be at the University of Florence, NEUROFARBA Department. The deadline is: 28th January at 13:00 (CET) The activity involves the use
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, you will have the chance to shape the future of AI security by developing novel defense mechanisms, verification methods, and security frameworks that can withstand adversarial attacks. Key
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detection, avoidance and path planning methods for mobile systems in any field Expertise on object feature detection, classification and localization Real experience on working with different perception
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possibility to be explored is the design of hybrid simulation systems in which the rigor of physical laws is integrated with the power and versatility of AI methods trained with on-field data, either annotated
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applications for one postdoctoral position in theoretical particle physics. Candidates with expertise in flavour physics, axion phenomenology, effective field theories, low-energy QCD methods, and particle