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your research at conferences, workshops, and through scientific publications. Your profile: ▪ You hold an MSc degree in physics, engineering, or computer science. ▪ You are very experienced in Python
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experiences in working with remote sensing data, climate data and programming skills (R or Python) are desired. You enjoy working in an international team and you are keen on developing a key set of research
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, ideally in Python, with demonstrated experience in applying them in complex research or development projects Basic knowledge in Machine Learning, ideally supported by initial hands-on experience Experience
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collaboration Relevant experience with programming languages (preferably Python) You work independently, in a structured and reliable manner. You are communicative, flexible and able to work under pressure
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview
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for challenging mathematical problems and interdisciplinary collaboration Relevant experience with programming languages (preferably Python) You work independently, in a structured and reliable manner. You are
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qualitative methods; some background in or willingness to learn computational social science methods as well as R and/or Python is highly desirable. Willingness to develop an ambitious empirical research
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, sociological...) to measuring beliefs, attitudes, and their relationship with shaping (online) behavior is desirable. Familiarity with data analysis in R or Python is required. Experience in study handling
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soil characterization techniques. A strong command of English is indispensable. Skills in R, MATLAB or Python are helpful. The effective exchange within the interdisciplinary collaboration will be
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discipline Strong interest in automation of process steps (Python, LabView, or similar... Experience in one (or more) of the following areas is desirable: programming, automation, synthesis of inorganic