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deeply committed to using that knowledge to make a difference in people’s lives. The department has 32 faculty members, some with dual appointments that enrich our transdisciplinary approach to design
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University. Requirements A master’s degree in (applied) mathematics (or related), with a strong background in computational methods, preferably also using computational frameworks for machine learning in
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synthetic fuel reactors. Tasks include gas handling, system diagnostics, thermal integration, and performance evaluation under variable power inputs. Data Analysis and Machine Learning: Collect and process
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of excellent research, innovative solutions, education or learning, we must make a positive difference to society and contribute to a sustainable future. We do this by cultivating talents and creating the best
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Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
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research areas. New insights and synergetic effects resulting from collaboration between inherently different viewpoints of separate fields typically accompany this endeavour. Our task force on machine
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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screening (Ulrike Haug), prevention and implementation science (Hajo Zeeb, Daniela Fuhr), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot
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Established record leading publications A collaborative approach and ability to work independently Experience with interdisciplinary methodologies and the integration of different data sources is preferred
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Project 2: Experience working with large datasets and machine learning Reasonable proficiency in any coding language used in data science Mixed-methods research experience Online Application Required