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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic
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: 01.10.2025 Application deadline: 03.09.2025 Tasks Execution of experimental work in a mouse model of cortical multiple sclerosis Application of in vivo imaging and quantitative analysis methods Investigation
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic
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set of alternative ways of evaluating a particular expression with unspecified matrix sizes. When a concrete expression is evaluated at run-time, thus revealing the matrix sizes, an extraction algorithm
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language and technology co-evolve. Many current language technologies bear the mark of a legacy that privileges text over talk, information over interaction, and algorithms over agency. In Futures
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consider the multiple health and other impacts when deciding on appropriate action. This PhD studentship will conduct an assessment of the impacts of policies aimed at improving home energy efficiency