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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
. The ideal candidate has research background and interests in one or more of the following areas: optimal transport (mathematical theory, computational analysis, machine learning applications), approximation
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(e.g. Python, R, …). Familiarity to work on a Linux computing cluster (HPC). Preferably experience in working with large medical image data. Vivid interest in the analysis of microscopy images or similar
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
. The fellow’s primary role will be to lead and support water quality research and lead the development of trainings and short courses on water quality monitoring and analysis. The Water Institute at UNC
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(MUCCnet: atmosphere.ei.tum.de ) Optimization of an urban sensor network configuration for greenhouse gas and air pollutant measurements using mathematical and physical assessments Analysis of ground-based
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to conduct research on two interdisciplinary research projects on transportation, environment, and health. The candidate is expected to work on data integration and fusion, develop theoretical and mathematical
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14.12.2022, Wissenschaftliches Personal The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics
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substrates and their innovative use in applications such as robotics, and wearable and interactive systems. Qualifications: The successful candidate having a strong foundation in at least a few of the
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gross monthly remuneration in pay group TV-E 13 is in the range €4,074 - €5,873, depending on work experience and length of service. In this project, the lower network layers and an associated integrated
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of the Agency. Potential areas include, but are not limited to: algorithmic engineering and high-performance computing; design and analysis of algorithms and data structures; discrete optimisation and operations
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(ML4Earth). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together