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performance, detect artefacts, and ensure scientific rigor Proficiency in Python (or Julia/R) for data handling, ML model development, and statistical analysis Ready to work with time‑series data, sensor fusion
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digitalized society, a climate-friendly energy system, and a resource-efficient economy. We combine natural, life, and engineering sciences in the fields of information, energy, and bioeconomy with specialist
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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analysis using microscopy and time-resolved spectroscopy, testing reusability of heterostructures, dielectric engineering concepts, and join work with sustainability experts; acquiring knowledge and tools
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network analyses experience is useful Experience in data analysis and scientific publication Ability to work in a team, good English communication skills A driver licence is useful Our offer We offer
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infectious diseases, antimicrobial resistance, medical decision making and population health metrics. Interest and experience in empirical applications of economic theory, the analysis of large health data
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Description Where: At the Chair for Computational and Theoretical Biology ( CCTB ) and the Center for Artificial Intelligence and Data Science ( CAIDAS ) of the University of Würzburg. Project
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performing data analysis of B cell receptor repertoire data using programming languages (mostly R) Successful candidates can think independently, are eager to acquire necessary new skills, and are excellent
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research area is the design and analysis of graph algorithms, with possible focus areas including parameterized algorithms, structural graph theory, graph symmetries and similarities, and algorithmic model
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in the STEM disciplines are primarily attributable to the genius of individual visionary figures or rather to synergetic team efforts. Through a systematic analysis of at least 50 key examples from