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modern control theory (PID, adaptive, robust, and fractional control); Experience with fuzzy logic and AI-based control (reinforcement learning, neuro-fuzzy systems); Skills in modelling and simulation
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demonstrate solid knowledge of relevant social science theories and methods necessary to carry out the proposed PhD project. Admission to the faculty’s PhD programme is a prerequisite. See admission
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fields may be considered on an individual basis. Candidates must demonstrate solid knowledge of relevant theories and methods necessary to carry out the proposed PhD project. Admission to the faculty’s PhD
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relevant fields may be considered on an individual basis. Candidates must demonstrate solid knowledge of relevant social science theories and methods necessary to carry out the proposed PhD project
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: Strong expertise in signal processing for wireless communication systems, including modulation and coding techniques on the physical layer of a radio, array antennas and their use in multiple input
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theory & experiment: Co‑design validation experiments with experimentalists; iterate models using feedback from new measurements. Automate the workflow: Build Python workflows for simulation and data
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developing synthetic datasets using simulation environments. Familiarity with the theory and implementation of diffusion models and GANNs applied to the same context. Minimum requirements: • Knowledge
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& advance digital twins: Integrate electronic structure (e.g., DFT, ab initio MD, tight-binding) with multiscale simulations to predict experimental observables at interfaces. Bridge theory & experiment: Co
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with Climate Crisis and Ecological Thought. Task 2: This task explores the theory and practice of artistic research and artistic creation within the context of climate change. Applicable legislation and
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the master's degree has been awarded. A strong interest in Algorithmic Number Theory and/or Cryptography is a requirement. Experience in programming is an advantage. Experience in developing and coding