36 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Leibniz
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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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teaching and curriculum development. Your qualifications PhD in computer science, data science, applied mathematics, physics, or a related field. Strong expertise in machine learning and deep learning
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in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
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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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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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Qualifications / Experience: • A PhD in Physics, Geoscience or a related field • Proven expertise in numerical modelling using super computing clusters • Excellent knowledge of atmospheric physics • Proficiency in
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interdisciplinary within a joint research program. What will be your tasks? The successful candidate will work closely with scientists, postdoctoral researchers, and doctoral students within the department, as
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the structured Junior Faculty Development Program and have the opportunity to teach at LMU Munich engage with international top-class economists within our CESifo network with over 2,000 members from all over