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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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the understanding of large-scale irrigation-atmosphere interactions. You will use state-of-the-art global satellite remote-sensing data to analyse the impact of irrigation on cloud and rain formation. This will
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developmental data and ecological validity. Infant data will be acquired in collaboration with Dr. Tessa Dekker (University College London) and Dr. Ingmar Visser at UvA. The project thus aims to quantify and
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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proficiency in R or Python for data analysis and modeling. Familiarity with analyzing large-scale healthcare datasets and real-world data. Experience in developing and applying simulation models, including
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Human Genome-Phenome Archive. The position will also be connected to a vibrant local ecosystem for data science and machine learning. Your Tasks The research group of Dr. Brian Clarke is looking for a
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in working with large data sets and the development of numerical models. Basic knowledge of glaciology or geodesy. Good expertise in programming, e.g. in Python, MATLAB, or other high-level programming
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power systems. You will join a large group of postgraduate students in the Faculty of Engineering, working on many aspects of solar energy and zero carbon technologies. The team of potential PhD
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 2 months ago
investigated by in situ X-powder diffraction, scattering techniques, and spectroscopic measurements. The in situ/operando experiments will be performed in our laboratories and at large research synchrotron
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if the necessary clean energy and energy infrastructure is available at competitive costs. Large-scale investments in the energy sector meanwhile get off the ground only if industry is prepared to electrify its