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innovation, robust governance and broad capacity building. AI LEARN is a consortium of 31 national and international partners from academia and the public and private sectors. About the project/work tasks
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develop knowledge that supports the hydropower industry in the safe design of future dams and management of existing embankment dams, including those subjected to flexible hydropower operation. Emphasis
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utilize low-grade waste heat. The project aims to develop methods for the design, optimization, and operation of integrated systems that maximize both economic performance and environmental benefits
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position will be part of the PhD program at the Business School (PhD in Social Sciences , specialization Business Administration, Innovation and Governance). The successful applicant is expected to complete
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communities Preparedness to plan and complete a demanding research project and doctoral education within the given time frame Motivation to contribute actively to the research environment, teaching activities
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Electronic System Design 4.6/5.0 Signal Processing for Anomaly Detection CV including information about education background and work experience Transcripts and diplomas for Bachelor's and Master's degrees
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approaches. While underground stope design is critical for safe and efficient ore extraction, it is often constrained by sparse geotechnical data. Conventional interpolation techniques frequently struggle with
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, transfer learning, uncertainty quantification and anomaly detection techniques. Design, evaluate and test automated control strategies (MPC, model-based and model-free RL) for optimal feeding and harvesting
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trustworthy AI and seeks to produce ground-breaking results that have been tested on real-world problems. In advancing trustworthy AI, TRUST will develop new AI technologies, support innovation, and investigate
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should also include a progress plan for the different parts of the project. Admittance to the PhD programme will be based on the research proposal. The candidate must also submit a PhD plan within three