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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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, Computational Neuroscience, Computational Psychology or Behavioural Science; Transport Modelling, Transportation Science or Urban Mobility; Data Science, Artificial Intelligence, Machine Learning
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of Power-to-X processes for renewable fuel production. The project is primarily rooted in process engineering while also addressing the interaction between Power-to-X plants and the electrical grid. You will
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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provides additional context for the assessment. However, this is not a requirement. Framework for the PhD Programme The PhD programme is conducted in accordance with the current Danish Ministerial Order on
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collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction
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data sources (e.g., registry data, surveys, and organisations). Your competencies Digital methods such as machine learning based classification, computational text analysis, network analysis, web
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of hydrographic and current measurements obtained using CTDs, ADCPs, moorings, and related observational platforms. • Experience in analysing large oceanographic and climate datasets, including observational and
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate