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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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researcher with a proven track record in areas relevant to auto-tuning, focusing on ML-driven compiler optimization, transfer learning, and programming for heterogeneous systems across CPUs, GPUs, and
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the contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular
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materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials synthesis and
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the MONALISA network and local courses, (iii) to write scientific articles and a PhD thesis, as well as (iv) to teach and disseminate research in the scientific community. Requirements: Applicants should hold a
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training activities within the MONALISA network and local courses, (iii) to write scientific articles and a PhD thesis, as well as (iv) to teach and disseminate research in the scientific community
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven
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opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD degree with experience in molecular biology, computational
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this project, we highly