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econometrics, high-dimensional statistics, machine learning, nonparametric statistics, portfolio theory and stochastic processes. Our PhD program comprises a 2-year MPhil Phase with courses aimed at building a
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the focus areas are stochastic optimization and equilibrium modelling in energy systems and markets. Position 1: PhD Project - “Optimisation of household demand response” The project aims to achieve
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useful background for candidates would include continuous-time stochastic processes, martingales, Brownian motion. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s
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Lunar Power Grid Advanced Control Strategies for Renewable Energy system A Circular Approach to Manufacturing Sustainable Powertrains for Wind Turbines PhD in Advanced Stochastic Control for for Renewable
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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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of stochastic systems, and possibly reinforcement learning / POMDPs; ● Has, or will soon acquire, skills in Python or R (or equivalent); ● Is willing and able to move between ENS in the Paris region and SETE in
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Lagrangian spontaneous stochasticity, anomalous dissipation, anomalous regularization, and others. Additional research directions can be pursued if desired, upon agreement with the PI. The ideal candidate has
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or non-parametric functional distribution for species responses to environmental drivers as well as latent factors to describe spatially and temporally structured stochastic processes. JSDMs are routinely
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results from numerical modelling of surface mass balance, firn compaction and ice flow dynamics identifying and quantifying processes of ice sheet change and ice mass balances developing stochastic
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stochastics group of the Korteweg-de Vries Institute for Mathematics at the University of Amsterdam is inviting applications via the Ellis PhD Program for a PhD position in mathematical machine learning