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Field
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-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and machine learning approaches. These are similar to earlier work
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of topics is covered, from large-scale data management to data mining and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 2 months ago
this PhD, we propose to apply statistical computing combined with machine learning (ML) to the spectrophotometric data to derive high-resolution information on CDOM absorption and its origin. This will be
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is possible in collaboration with other members of the lab. Desired (but not absolutely required) skills: programming in python, machine learning, and experience in protein structure analysis. Required
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of Excellence iFIT Cluster of Excellence Machine Learning Cluster of Excellence TERRA CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with