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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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provide the possibility for the student to work with LLMs and machine learning. Your competencies Interest in learner centered technology design, in particular how AI systems can scaffold reflection, agency
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such as achievable rates, coverage, and energy efficiency, as well as optimize relevant system parameters. Applicants must hold a PhD degree in Electrical/Electronics Engineering, telecommunications
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Classification Title: Lecturer/Sr Lecturer/Master Lecturer Classification Minimum Requirements: PhD in engineering or a closely related discipline Job Description: The Department of Engineering
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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Engineering & Development, Artificial Intelligence/Machine Learning platforms, Blockchain, Cloud Computing, Software Engineering, as well as Data Architecture & Engineering. In this role, the Teaching Faculty
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of the Institute of Chemical Technology ITQ (CSIC-UPV) in Valencia offers a PhD position linked to the project entitled "AI-Enabled Defect Engineering and Mechanistic Discovery in MFI Zeolites", funded by the “la
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of the Software Engineering unit . About the project This PhD fellowship is part of the national AI Centre for the Empowerment of Human Learning (AI LEARN). AI LEARN is a new Centre dedicated to the empowerment
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on tungsten samples and candidate tungsten alloys will validate the simulations and guide the design of more dust-resistant materials. Finally, we will use machine learning to integrate simulation and
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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