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is looking for a postdoctoral researcher, doctoral researcher or project researcher to develop machine learning methods for health data analytics. The fixed term position starts on mutual agreement
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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Scientist will design and conduct experiments on human planning strategies, analyze data, prepare manuscripts, and be part of the research community in the laboratory of Prof. Wei Ji Ma. In compliance with
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researchers, under the supervision of Prof David Wedge. Collectively, this team has expertise in the analysis of multilevel omic and imaging data; data integration and machine learning; risk prediction. This
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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researchers, under the supervision of Prof David Wedge. Collectively, this team has expertise in the analysis of multilevel omic and imaging data; data integration and machine learning; risk prediction. This
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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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-Phenomenology (hep-ph) , HEP-Theory (hep-th) , High Energy Physics , High Energy Theory , Machine Learning , Particle Physics , String Theory/Quantum Gravity/Field Theory , string-math Appl Deadline: 2026/03/31
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understanding and practical experience with machine learning approaches for biomarker discovery and predictive modeling, specifically with hands-on experience in developing and applying neuronal networks