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quantitative toxicity assessment in key industrial settings like drug development. In AUTOMATHIC, we aim to develop an integrated framework for automated ODE structure identification, parameter estimation and
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on cutting-edge topics such as: Data-efficient learning Explainable AI Memory-efficient deep learning Energy-efficient deep learning Parameter-efficient fine-tuning of foundational models The PhD studentship
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, economics and statistics for explaining and predicting human behaviour and decision-making using both choice data and neurophysiological process data, Devise and evaluate new estimators for parameter
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stability/lifetime of perovskite solar cells strongly depends on many—largely unknown—parameters. This is a problem in any research lab, but even more so at the industrial scale. This project will tackle
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. Second, simulation of pulse propagation in cables with variable parameters quantified in experimental studies. Third, utilizing signal processing and machine learning to develop detection and
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grain transport and energy transfer therefore govern these complex physical processes, which consist of: i) granular flow regimes and solid loading control parameters (Piton et al. 2015, Kozacovic et al
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in estimating parameters and states, as well as controlling these systems, arises from the fact that measurements and actuations are typically confined to the boundaries of the domain. This limitation
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respect to drought and Nitrogen-deficiency. Next, the candidate will construct a predictive hybrid model by (a) retrieving estimates for performance and resilience-defining parameters from experimental time
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system that produces high quality audio? Can we adapt the music synthesis dynamically to external parameters (e.g. in a gaming environment, make it dependent on the situation in the game)? Can we create a
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suicidal behaviour frequently co-occur (O’Connor et al., 2018), though it is acknowledged that establishing temporal relationships between constructs is necessary as it is not yet understood. It is estimated