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14 Mar 2026 Job Information Organisation/Company INFN - Istituto Nazionale di Fisica Nucleare Research Field Physics Physics » Other Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 19 Mar 2026 -...
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27 Jan 2026 Job Information Organisation/Company università degli studi di udine Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 10 Feb 2026 - 13:00...
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11 Feb 2026 Job Information Organisation/Company Università degli studi di Ferrara Research Field Engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 25 Feb 2026 - 12:00...
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will be involved in the development and implementation of machine
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of Business Intelligence (machine learning & LLM) to enhance the sustainability of regional tourism. On the supply side, a monitoring system is developed by extracting data from online platforms such as Google
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning
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of Computing Sciences. The project focuses on studying systems where multiple machine learning algorithms interact on shared platforms, aiming to characterize their behavior and build theoretical foundations
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. The department welcomes candidates specializing in Statistics, Machine Learning, or Probability. The preferred start date is September 2026, with possible flexibility to February 2027. The position is based in a
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), textual analysis, and machine learning will be used to identify recurring patterns present in textual data. The objective is the development of a multi-level analysis that will link AIWM technologies
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efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices. Still, many challenges are ahead