65 programming-"https:"-"Inserm"-"FEMTO-ST"-"https:"-"https:"-"https:"-"https:" positions at University of Vienna
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conferences and journals is a plus. You are an experienced programmer, preferably using the Python programming language. You are a team player with excellent social/communication skills. You have experience
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for optimization under uncertainty, in particular decision-focused learning. • You are an experienced programmer, preferably using the Python programming language. • You are highly organized and have excellent
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Neural Networks (PINNs), Neural ODEs, Bayesian learning, and system modeling and simulation Strong background in numerical methods and machine learning Good programming skills, preferably in Python
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successfully publishing academic research at top OR journals (e.g., European Journal of Operational Research) You are an experienced programmer, preferably using the Python programming language. You have
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on a topic relevant to the department, such as fertility and family, mortality and health, migration, or population analysis and human capital. The candidate should present a clear personal work plan for
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. Method development: Develop and optimize pulse programs and parameter sets for organochemical, biochemical, and materials applications; support the implementation of full automation workflows
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skills, including MS Office and other programs e.g. photoshop High-resolution confocal imaging experience Basic programming skills Excellent knowledge of Drosophila genetics Very good ability to explain
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the main research areas of the group (digital transformation, sustainable and healthy consumer behavior, psychology of brand management) Teaching experience Experience in programming experiments Excellent
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programme of the Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (VDS PhaNuSpo) and to sign a doctoral thesis agreement within 12-18 months after being employed. The position is
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to the department Ph.D. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods