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are undergoing rapid changes, with increasing adoption of distributed renewable generation (from PVs and wind turbines), new forms of demand (from EV charging, heating) and storage. This poses significant
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, stress markers, EEG, and ECG — will be collected by VR headsets and IoT devices. ML algorithms will analyse this data to identify trends, project risk factors, and propose tailored treatments. By combining
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frameworks to ensure the developed processes are compliant, scalable, and environmentally responsible. Multiobjective optimization algorithms will be employed to balance key performance indicators such as
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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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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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technologies used in and connected to grids (e.g., distributed generation, storage, electric vehicles, heat pumps, smart energy management systems, etc.) and how to design grid tariffs that align grid and market
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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
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seismicity and landslides. Application: Scaling up to assess the spatial distribution of long-term hydrological changes across Switzerland. As a final project goal stands the development of a real-time
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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