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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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and functional materials. UV photopolymerisation, and more recently LED technologies, enable rapid, localised and energy-efficient processes. Recent developments in UV LEDs at different wavelengths, as
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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tomography, high-pressure reactor) with numerical modelling (COMSOL) to quantify and predict dissolution. • Select and characterise biogenic shells (foraminifera, pteropods) from oceanographic cruises
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population prediction models and their impact on the epidemic differ significantly from indicators (R₀ estimation) based on simple ODE models calibrated on general population sampling data (approximately 1,000
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topology changes and transitions between different operating modes also constitutes a major challenge. Furthermore, this thesis will focus on the frugality of the proposed approaches, seeking to reduce the
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climate models. Paleoclimate reconstructions allow us to describe natural hydroclimatic changes in the Amazon under different forcings. Existing records reveal past variations in Amazonian hydroclimate
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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physics, biophysics and climate physics. Roughly 30 new doctoral students and postdoctoral researchers join the Laboratory every year. Sea-level projections are derived using Earth-system models (ESM
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will have covered more than one Jovian year (11.86 Earth years), as well as more than one solar cycle (on average 11.2 Earth years). The study of the different components of radio emissions as a function