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Science or Intelligent Systems Engineering Department. We seek candidates who can teach and lead cutting-edge research in AI, including generative AI, to bridge biomolecular data—such as genomics and pharmaceutical
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of fungal communities, working with “big data”, and characterising soil physicochemical properties. You will join a diverse, active and collaborative research group in the Soil and Ecosystem Ecology Lab
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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specifically of both hedonic and causal machine learning methods for modelling housing market values and familiarity with appropriate data sources to support these. Skills Essential C1 Ability to communicate
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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willingness to acquire advanced longitudinal and machine-learning methods. Programming skills in R (or a demonstrated ability to acquire them quickly). An interest in working with large, real-world data from