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advanced predictive control methods to optimize the operation of an integrated system of hybrid energy storage systems (i.e., multi-carrier energy hub or micro-grid) and high-tech greenhouses. These hubs
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manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance
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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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to genomic and epidemiological data; design controlled computational experiments (simulations and synthetic datasets) to validate theoretical predictions; apply your methods to large-scale viral datasets
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different rescripting strategies reduce the impact of emotional memories, and whether these changes in memory predict subsequent symptom reduction in a student population. This work will be followed by
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. The lack of knowledge is related to the models that should be used to auralize UAM in urban environments: new models are needed to predict noise exposure in urban cities. Traditional aircraft noise studies
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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crystallization from mixed salt solutions and elucidate salt weathering mechanisms at micro and macro scales in heritage materials. Your work will bridge the gap between theoretical predictions and real-world
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recrystallization and control nucleation for biomedical applications in e.g. heart and kidney cryopreservation. Where to apply Website https://www.academictransfer.com/en/jobs/359905/phd-in-de-novo-design-of-ice-bi
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MRI measurements can be translated into meaningful input for predicting optimal sensor phase configurations and feedback control; Identify pathways towards the integration of domain knowledge about MRI