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and BAT.jl projects. The position also offers opportunities to contribute to research in Bayesian inference and its application to physics in general. The DEMOS project aims to develop state-of-the-art
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 12 days ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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, or another relevant area of expertise. The successful candidate is expected to have a profile along one of the following lines:1) Strong quantitative background with skills in causal inference or other
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Potsdam, Brandenburg | Germany | 25 days ago
inference, network analysis, or other advanced statistical techniques Further Information The AWI is characterized by The AWI is characterized by Our scientific success - excellent research Collaboration and
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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resides in is to build up technology for data-driven pattern recognition to infer data-driven generalized auditory profiles. The central challenge for this endeavor are the small sample sizes (of typically
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for inferring signatures of natural selection. The position is supposed to be filled at the earliest possible date and initially funded for 2 years with the possibility for extension. Compensation with all public
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, paleobiology, geoecology, evolutionary research, or a related field Experience in computational modeling, analysis of ecological or evolutionary data, or causal inference Proficiency in model calibration
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA