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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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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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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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website: https://internationalstaff.au.dk/prearrival/ for information about arriving to and living in Denmark for both the applicants and their potential families. Application procedure Short-listing is
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: Motivated cover letter Special PhD application form (available here ) Curriculum vitae Bachelor's and Master’s degrees or equivalent (copy of original + verified English translation) A complete list of
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degradation modes. Evaluating suitable sensor technologies and data sources for acquiring relevant metrics. Developing tools and algorithms to automatically analyse sensor data, assess asset condition, and
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an optimal molecular representation (including data procurement) and integrating generative model and binding oracles. Propose an algorithm to bias the generative models towards desirable properties, such as
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the above list of PhD projects or individual research projects). Incomplete applications will not be considered. You may apply prior to obtaining your master's degree but cannot begin before having received
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English) including official description of grading scale List of 1-2 academic references A writing sample, e.g., MSc thesis (or draft), BSc thesis, project report, or similar illustrating your work and your
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activities with industry partners Besides a passion for renewable energy and sustainability, a successful candidate will fulfill at least 3 points in the following list of qualifications: A strong background