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modelling and improving Earth System Modeling by better merging of measurement data and model simulations. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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services. The innovative application of machine learning to exploit satellite data within numerical modelling links to other scientific applications beyond those of the atmosphere. The project builds
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. Faculty cover a wide range of research topics and methods and belong to the top 10 ISOM research groups in Europe . You will be able to benefit from the department’s strong research focus and its numerous
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humidity sensor. Perform supervised original research to learn and practice air-sea interaction, temperature and humidity measurement techniques, saturation specific humidity, numerical inversion methods
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focus and its numerous resources (e.g., databases, research seminars, brown bags, career planning, skills-enhancement workshops, international conferences, contacts with practitioners, etc
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framework capable of accurately predicting pollutant transport and dispersion in coastal waters. By combining high-fidelity numerical simulations with data driven surrogate models, the proposed research aims
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their area of interest as part of their research proposal which accompanies their graduate application. You will work within the CoTide team using a range of numerical modelling and optimisation techniques
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fabrication, with the ability to prototype and test light delivery and optical feedback systems. This role also involves conducting numerical modelling of light propagation in biological tissues, contributing
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) • An occupational health management model with numerous attractive options, such as our university sports programme • Supplementary pension scheme (RZVK) • Discounted tickets on local public transport services (‘Job