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acquisition tools, signal processing methods and advanced data analytics. The successful candidate will also develop computational models of light transport in biological tissues, including Monte Carlo-based
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research fellow to support ongoing research projects in diffusion generative models and Monte Carlo stochastic simulation methods. This position holder will apply these techniques and other advanced AI
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MST data, focussed on mirror alignment and optical performance validation by comparing data to Monte Carlo simulations What you bring to the table: - completed scientific university education and
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interfaces (this project includes a simulation interface deliverable, developed by the Research Fellow with support from the project team). Experience with Monte Carlo and sensitivity analysis. Experience
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to nonperturbative phenomena—are equally encouraged to apply. Experience with numerical methods (e.g., MPS/tensor networks, Monte Carlo, exact diagonalization), scientific programming (Python/Julia/C++), and
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] - Dynamical Mean-Field Theory (DMFT) and Density Functional Theory (DFT) - Density Matrix Embedding Theory (DMET) - Density Matrix Renormalization Group (DMRG) - Quantum Monte Carlo (QMC) - Tensor network