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Field
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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research
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as well as profound knowledge of professional computer-aided design and 3D modelling In addition, you have experience in CAD/CAM (preferably McNeel Rhinoceros) and/or robotic fabrication, as
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https
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, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https://www.unibas.ch/en
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. PhD Project Description Project Title: Photonic-Inspired Federated Learning for Communication-Efficient Next Generation Radio Access Networks (NG-RANs) Description: This PhD project will develop a novel
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environments This PhD project investigates the use of digital technologies (environmental sensing, user feedback loops, computer vision, machine learning) and theories of human perception and behavioral nudging
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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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enrolled in the Spring semester as a Graduate student at the University of Oklahoma. Hiring continent upon verification of current student status. Must attach Spring 2026 class schedule Learning Outcomes
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This