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https://phd.fbk.eu/calls/detail/artificial-intelligence-and-machine-learning-fo… Requirements Research FieldOtherEducation LevelMaster Degree or equivalent Additional Information Work Location(s) Number
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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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for more than 12 months in the 36 months immediately prior to your recruitment. Skills: Strong interest in AI/Machine Learning, Bayesian modeling and decision-making. Benefits Competitive Salary: Living
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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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
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activities. Where to apply Website https://jobs.fbk.eu/Annunci/Offerte_di_lavoro_Tenure_track_position_for_a_Resea… Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent
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Infrastructure? No Offer Description Organization / Company: Università di Pisa (UNIPI) Department: Dipartimento di Informatica (Department of Computer Science) Research Field: Computer Science; Machine Learning
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-ter’s, and PhD programs of the Department, specifically in the fields of Management Engineer-ing and Innovation. It may also include advanced-level courses taught in English, closely aligned with
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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network structures. Methods from graph theory, machine learning, and artificial intelligence will be employed to model complex relational structures and identify patterns in high-dimensional data. The work