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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human participants; possess versatile
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent academic record, including
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for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k