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properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
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apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
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-series modeling (EEG, video, sensor data) and chemical/structural data representation (e.g. graphs, SMILES strings, molecular embeddings). Familiarity with multimodal representation learning and
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integrated sensor arrays. The project combines several different concepts: Progress in understanding insect neurobiology that provides proven circuit designs to solve significant problems such as autonomous
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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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interpretation of results. You will also tailor these analyses in response to clinical and researcher feedback, and help develop new algorithms where needed: this may include the incorporation of genomic or other
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, or equivalent, with excellent knowledge of digital communications and signal processing. High grades in the core courses are required. Skills in mathematical analysis, modeling, and network algorithms
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on Bayesian methods for real-time, risk-aware trajectory planning in autonomous driving. Develop, implement, and evaluate algorithms for scenario pruning, control action selection, and reachability analysis
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of insects for more than a decade, both in terms of engineering field deployable sensors and also in terms of fundamental understanding of light interaction with free flying insects. We now aim to push
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driving. Develop, implement, and evaluate algorithms for scenario pruning, control action selection, and reachability analysis. Compare advanced deep learning–based methods with probabilistic approaches