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a form suitable for quantum computing, calling for a cost-benefit analysis of quantum machine learning algorithms. To address scalability for large datasets, lossy data compression is often
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the high cost of representing large datasets in a form suitable for quantum computing, calling for a cost-benefit analysis of quantum machine learning algorithms. To address scalability for large datasets
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comprehensive training in translational liver research, including data analysis, experimental design, and scientific dissemination. Qualifications We are looking for a highly motivated PhD candidate with a strong
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laboratory experiments. The candidate will participate in all aspects of the project including acquisition of data from the literature, experimental work, modelling, data analysis, writing etc. The project can
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of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting
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terms of safety and learnability Develop algorithms to detect and characterize harmful market outcomes Design and test market rules that steer multi-agent learning dynamics Combine theoretical analysis
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analysis. Experience with programming tools like Python and energy system modelling tools like PYPSA is considered an advantage. The successful candidate should possess a curious and interdisciplinary
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. metabolic rate (respirometry) and thermal preference and avoidance Investigating growth rate and thermal experience from analysis of otoliths Analysing data using R or similar statistical software. Writing
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, or equivalent) in Public Health, Medicine, Pharmacy, Epidemiology, or a related discipline • Strong interest in epidemiological methods, data analysis, and coding (e.g., R, Stata, or similar) • Interest in
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. The duration of the position is three years. The PhD stipend is available within the general study program "Computer Science". Your work tasks The main tasks for this PhD project consist of requirements analysis