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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 19 hours ago
inference, including machine-learning methods. Astrophysics of compact objects and binary formation scenarios. Cosmography with gravitational waves: dark energy, dark matter and gravitational lensing. Tests
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PhD in one of these areas. Candidates should have an exceptional academic record, a strong background in machine learning, and a robust mathematical foundation. They are also expected to have strong
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only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits, such as public transport season tickets and car sharing, a wide
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, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure for streamed data that enhance contemporary generative and large
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machine learning approaches to build a system that highlights promising locations for archaeological research. You are also expected to play an active role in project coordination and in writing follow-up
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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evolution. ● Experience with population genetics. ● Experience developing R packages. ● Experience applying or developing machine learning tools. ● Strong scientific writing skills. Brief
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Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your
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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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Weather and climate prediction are undergoing a profound transformation. Alongside traditional physics-based forecast systems, machine-learned (ML) weather prediction models, hybrid ML-physics