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Field
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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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of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in
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models to characterize agricultural and ecological systems; Experience in applying advanced Artificial Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in
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to research, development and demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product
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Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time
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Research Framework Programme? Horizon Europe - ERC Reference Number 101166983 Is the Job related to staff position within a Research Infrastructure? No Offer Description This is NTNU NTNU is a broad-based
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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | 2 days ago
the IMPRS CoNI is English, German language skills are not mandatory. For PhD applicants, a minimum score required for a TOEFL paper-based test is 550. The minimum score for the TOEFL computer-based test is
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, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were prepared, experienced and
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with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order manufacturing. This can be proven by the attendance