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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
apply machine learning/AI methods for ecological analyses Expedition experience Further Information The AWI is characterized by The AWI is characterized by our scientific success - excellent research
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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subjects, high interdisciplinary desire to learn, and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills (good command
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supported by an external team of deep-learning experts. You will also become an integral part of the Multiscale Cloud Physics Group currently being established by Dr Franziska Glassmeier at the Max Planck
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. Profound knowledge of at least one programming language, preferably Python. Previous experience in machine learning and deep learning. Practical experience with frameworks such as Keras or PyTorch Good
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data science, computer science, physics or a related field. Profound knowledge of at least one programming language, preferably Python. Previous experience in machine learning and deep learning
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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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external forcings on climate analysis of climate model output by means of classical statistical and machine-learning methods coordination of scientific workflows among project partners Your profile Master's