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methodological focus will be the precise determination of the number of confined water molecules, addressed through two parallel strategies: connecting the channel to a fixed-pressure reservoir, and the
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infer excitation conditions and quantify the impact of fast, tenuous shocks on ionized line emission. In parallel, high-resolution 3D MHD simulations of fast shocks interacting with dense interstellar
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functions such as memory and object recognition, as well as consciousness and mental states. This research has several distinctive features: it employs a parallel approach involving human and non-human
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computational perspective, this requires solving billions of systems of ordinary differential equations in parallel on adaptively refined meshes, while performing interpolation and tensor operations
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lake and river water levels. In parallel, socio-environmental signals on water-related extremes and pressures will be extracted using natural language processing and text-mining techniques, drawing
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a semi-industrial facility. In parallel, this PhD research will focus on investigating and optimising the process for removing arsenic from effluent, including through flocculation. Consequently
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enthusiastic about computational imaging and have experience in solving inverse problems. You enjoy scientific programming and thrive in collaborative, international environments, while also being able
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molecular programming, microfluidics, and engineering to design sophisticated algorithms for exploring and manipulating information encoded in DNA-based graphs. The position is fully funded by a
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modeling or EEG is an advantage, but is not required. We welcome applicants who are motivated to develop strong programming and quantitative skills. A Master's degree, completed by the start of the
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Eligibility criteria Core Competencies: - Functional ecology, stability theory in ecology, dynamical systems, linear algebra, data analysis. Operational Competencies: - Scientific programming, writing