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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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statistical inference. More specifically the recruited PhD student will consider variational inference approaches for GEA and stochastic optimization to speed up the inference, with the objective of scaling up
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and beyond; VHE) and producing multimessenger signals (e.g. photons and neutrinos). Indeed, the pervasive turbulence can ensure efficient stochastic particle acceleration, while the ambient backgrounds
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