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partners, as well as supporting proposals for funding of national/international applications. Required Qualifications PhD in Software Engineering, Robotics, Computer Science, AI, or closely related areas
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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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technical skills and knowledge: Computational methods for data analysis Quantum Computing theory (e.g., QC algorithms) Practical Quantum Computing (e.g., Qiskit) Software development in Linux The following
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interpretation; Preparing numerical and graphical summaries (visualizations) using relevant software and libraries for dissemination to both scientific and broader audiences; Assisting with grant applications
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. This includes designing custom-built optical microscopes, developing control software, and building data analysis pipelines to extract quantitative information from dynamic samples. Within the research team, your
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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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project will construct and credibly analyze the first comprehensive data set on the agrarian innovations, formal and informal institutions and alternative economic activities developed by the first recorded
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the building, as are key core facilities including deep sequencing, flow cytometry cell sorting and hematopathology. The candidate should have a PhD or equivalent research experience. He/she should be
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methods for the solution of differential equations. Where to apply Website https://www.unibo.it/it Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows
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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