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Field
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control algorithms for whole system efficiency optimisation. Design and simulate power management circuits using tools like SPICE or MATLAB/Simulink. Prototype and test circuits with real energy harvesters
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student, you will be embedded in the research groups Computational Imaging and Deep Learning (CIDL) and Applied Quantum Algorithms (aQa), part of the Leiden Institute of Advanced Computer Science (LIACS
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properties of skeletal muscle during static and dynamic contractions. The student will also participate in early-stage algorithmic work to model muscle architecture and behavior across contraction types. In
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to the full development pipeline: from algorithm design and implementation to clinical integration and evaluation. You will also work on improving prognostic models using (neuro-symbolic) AI and develop
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm