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Field
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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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of the project is to develop new theory and algorithms for massive datasets, with a particular focus on online algorithms related to numerical linear algebra. Key Responsibilities: The main duty of this position
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research and travel expenses. Relevant areas of expertise include: algorithms and complexity, natural language processing, and knowledge of the algorithmic fairness literature. The postdoc will be mentored
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: The successful applicant will be responsible for: Obtaining rigorous mathematical results at the interface of optimization, geometry, and data science Designing, implementing, and testing algorithms Engaging in
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decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both
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literature review, algorithm design, experiment design, model training, evaluation, and benchmarking. Work with and supervise undergraduate or graduate student assistants, guiding them in data collection
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organisations from across Europe. Horizon EUROPE projects UPCAST and DATAPACT aim at designing and deploying compliant-by-design algorithms for data marketplaces and data sharing platforms, to collaboratively
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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organisations from across Europe. Horizon EUROPE projects UPCAST and DATAPACT aim at designing and deploying compliant-by-design algorithms for data marketplaces and data sharing platforms, to collaboratively