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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | about 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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modelling skills to make a real-world clinical impact on early disease prediction and personalized medicine. Main duties and responsibilities The postholder will lead the numerical model development and
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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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, climate science, physics, mathematics, or a related field (PhD is advantageous) Experience with numerical modelling and analysis of large simulation datasets Knowledge of ocean biogeochemistry or Earth
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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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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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, cardiac CT, ECG, portable ultrasound, and multimodal clinical data. Building scalable pipelines for image ingestion, DICOM/PACS interoperability, preprocessing, annotation, quality control, model training
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at the Laplace laboratory, where numerical and analytical models are being developed to predict plasma potential control and flux entrainment from polarized electrodes. During the third year of the thesis project
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research programme in fundamental AI, AI co-scientists, and applications in complex scientific domains such as RNA sequence-function modelling, structure prediction, and inverse design. Further information
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methodological comparison between mechanistic vector control models and macroscopic SIR-type models calibrated on serological survey data. Currently, risk indicators (e.g., R₀ estimation) derived from mosquito