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Field
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical and laboratory free text into structured terminology. The project combines classical text algorithms, medical
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Job Description Are you interested in working at the intersection of optimal control, AI algorithms, and game theory, on problems that matter for real-world critical infrastructure? This PhD
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(IQU, Spain) DC2: Design of novel distributed algorithms for CSI estimation (KUL, Belgium) DC3: Synchronization methods for CF networks (KUL, Belgium) DC4: Design of holistic neuromorphic solutions in 6G
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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algorithms. Investigate the use of LLMs as agents or interfaces for querying building data and optimizing control parameters. Validate methodologies using real-world data from built environment monitoring and
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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types. Your algorithms will first be evaluated through simulation using real operational datasets, and later deployed and tested at two physical facilities: a kW scale testbed at TU Delft’s Green Village
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research