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modelling, ecological forecasting, time-series analysis, Bayesian statistics, and statistical programming (primarily in R and Stan). You will gain experience working with large, long-term, multidimensional
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or more of DFT, molecular dynamics, multiscale/materials modelling, or machine learning for the sciences including, Bayesian methods, uncertainty quantification, scientific AI workflows, automated discovery
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increasingly popular in recent years. The recent introduction of OpenAI’s ChatGPT made large-scale models available to the public, which enables the integration of AI in everyday objects and tasks. As a result
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improving robustness. The project will explore strategies related to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and
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a Training and Supervision Plan, to structure the 4-year PhD trajectory. Perform the PhD research along four objectives: Analyse reward schemes for biodiversity – considering multiple actors (farms
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-driven optical spikes, enabling sensing systems with unprecedented speed and energy efficiency. A central objective of the project is to develop highly sensitive photodetector neurons by combining resonant
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optical energy required for an operation. However, critical slowing down and enhanced fluctuations may limit processing speed and accuracy. The main objective of this PhD project is to quantify this trade
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made large-scale models available to the public, which enables the integration of AI in everyday objects and tasks. As a result, the model scale is doubling yearly, causing a proportional increase in the
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plant health policy and practice. Research objectives The PhD research addresses the following objectives: Behavioural analysis: Identify and analyse behavioural drivers influencing stakeholder decisions
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of the way. With 3,200 m² of state-of-the-art lab facilities, housed in a museum with more than 40 million natural history objects, dedicated mentorship, and access to our broad international network