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design, open science practices) and data analytic abilities (for example, meta-analysis, simulation studies, multilevel and/or mathematical modeling) In-depth knowledge of (statistical) programming tools
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, segmentation, and severity quantification. The performance of AI models will be assessed across different impact energies, materials, and boundary conditions. Cranfield University is uniquely positioned
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, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification approaches. The goal
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that different insights lead to innovative solutions. We create a work environment where everyone feels welcome, safe, and appreciated, regardless of background, identity, or experience. Together, we are building
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engineering, applied physics, or applied mathematics, with proven experience (courses, projects, and work experience) in the field of wind energy or aerospace engineering. A combination of good mathematical and
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-ON-BRAIN) that connects different countries with the overarching aim to develop cutting-edge human in vitro and in silico biomedical tools to better understand the biology of brain disease/disorders. You
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, including characterization of electrodes using spectroscopical and electrochemical techniques at different levels from micro-scale to macro-scale. You are expected to finish the project with a PhD thesis and
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, or applied mathematics, with proven experience (courses, projects, and work experience) in the field of wind energy or aerospace engineering. A combination of good mathematical and analytical skills, a strong
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, extinctions, and environmental change; ● Running simulations and scenario analyses to explore how different discounting rules or time preferences shift optimal conservation choices; ● Fitting models