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Field
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candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | 2 months ago
laboratory microbiology. Experience with network analysis, predictive AI models, and multi-omics integration is desirable. The contract (30 h/week, 4 years) offers the opportunity to complete a PhD thesis in a
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, protein-excipient interactions, and process-related stress responses. Apply machine learning, AI, laboratory automation, and advanced data science to predictive modeling, workflow acceleration, and decision
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interferometry, etc.), (v) Predictive modelling of coupled phenomena (reactive transport, rock-water interactions, etc.), (vi) Uncertainty quantification (Monte Carlo, meta-modelling), and (vii) Risk analysis. The
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qualifications: Degree in oceanography, climate science, applied mathematics, computational physics, or a related discipline (PhD is advantageous) Experience with numerical modelling and analysis of large
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reliably assess fatigue damage and predict remaining lifetime, enabling proactive maintenance strategies that extend service life and reduce CO2 emissions. Through this PhD scholarship, you will contribute
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work closely with an interdisciplinary team spanning microbiology, engineering, and computation, and will contribute to developing predictive models that link bacterial physiology to infection outcome
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, these flows remain poorly understood. As a result, even the most basic properties cannot be predicted reliably. For instance, the best available models over- or underestimate the measured pressure drop in a
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on temporal data (predictive maintenance, sensory processing for robot control, etc), in which efficient on-device processing is crucial. We are looking for a highly motivated PhD candidate with an interest in
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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and