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bioinformatic skills to predict the evolution of rare diseases? FSHD is a rare neuromuscular disorder. No approved treatment is currently available. Slow and variable disease progression complicate trial design
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(ToF-PET) provides critical functional and molecular insights to improve cancer staging but is currently limited by detector timing resolution and sensitivity. Metascintillators, an emerging family of
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, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information. Various modern ML paradigms are proposed to address the diverse
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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described by patients, there are few interventions that are currently in place to support recovery. To determine how to best intervene in a cost-effective way, we need up to date cost estimates and quality
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, current models only predict the potential for events rather than actual specific landslide occurrence. These models also struggle to directly quantify landslide hazards and to address key characteristics
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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treatment, material and energy flow analysis, integrated data modelling, systems dynamics modelling, circular economy, sustainability assessment performance, decision-support tool design Month when Interviews
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of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise