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of the following areas: state models, time series analysis, computational statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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strategies: Leveraging traditional and causal machine learning approaches to determine which patients are most likely to benefit from specific therapies. Digital pathology and image-based analyses (starting
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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to the development of the research milieu. Requirements PhD degree in a field closely related to the position (e.g., computerized image analysis/processing, machine learning, artificial intelligence, data science
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Railway Engineering is seeking a motivated and collaborative postdoctoral researcher for a project on developing machine learning tools for pavement management. The project is conducted in collaboration
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for the doctoral degree. Exceptions from the 3-year limit can be made for longer periods resulting from parental leave, sick leave or military service. The following experience will strengthen your application
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial. About the employment The employment is a temporary position of 2 years according to central
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, and doctoral students active on both campuses. Learn more about the Department of Archaeology, Ancient History, and Conservation here: Department of Archaeology, Ancient History and Conservation