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tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement learning strategies for comb generation
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focus on the following areas: Subspace tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement
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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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. The objective of the PhD study is to investigate and address key research and design challenges, including but not limited to: Efficient communication models within timing constraints in quantum applications
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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developing 3D vision algorithms for object detection, recognition, and scene understanding to support planning and task execution in dynamic environments. Publishing research findings in leading international
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project position is the design and numerical simulation of quantum-dot-based single-photon sources using advanced optical simulation tools. The objective of the PhD2 project is the microfabrication
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institutions with the objective of receiving a doctoral degree (PhD). State-of-the art, exciting research in an international consortium with highly integrated research projects. Expert training in basic and
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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offered in this context, with the objective of modelling, coding, and field-validating a new mechanistic analysis tool for pavements containing fungal-bound granular layers. The research will focus on urban