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Field
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough
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want to contribute to the next level of molecular computing? Are you excited about the application of AI tools to train molecular systems how to process information? Then join our team as a PhD candidate
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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage
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several processing units with variable memory, can be profiled to pool the resources. The analytical systems, developed on data collected by onboard sensors and software triggers, can assist the operating
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the harmful effects of deepfakes on ordinary people. You will draw on methods from psychology, linguistics, data science, and computer science, such as behavioural analytics, neuroimaging, face and voice
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focus is demonstrating how predictive analytics can improve maintenance and production planning. By comparing data-driven and traditional methods, the project will highlight the tangible benefits
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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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scan data is desired Familiarity with analytical tools such as R or Python for statistical and phylogenetic comparative analyses Experience with scientific writing and publishing in peer-reviewed
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interpretation of the data generated and that’s where this project comes in. You’ll be applying metagenomic techniques to respiratory samples and developing analysis and interpretation approaches that facilitate
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Chemistry, Physics, Materials Science, or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject and the ability to think analytically and