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Field
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, Astronomy, or a closely related field is required. Experience with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must be submitted
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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knowledge for precision medicine in psychiatry, building on advanced statistical methods for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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out field studies, collecting and analyzing large data sets. · Use and evaluate AI tools to support research and training. · Create engaging, science-based training materials in both English and Spanish
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Information: Some travel required BNL policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience
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studies. Proficiency in relevant computational tools and statistical methods. Experience with machine learning in large datasets. Interest and motivation to work in a multidisciplinary team. Ability to work
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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carry out field studies, collecting and analyzing large data sets. Use and evaluate AI tools to support research and training. Create engaging, science-based training materials in both English and Spanish