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like LiteBIRD and FOSSIL. We have a large international network of collaborators, and as a doctoral fellow you will have many opportunities to travel and collaborate with researchers at other
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– a public-private partnership conducting phase II trials of new regimens for the treatment of tuberculosis (https://www.unite4tb.org/). Application of Bayesian methods for evidence synthesis
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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; maintain version control and prepares data for submission to public repositories and collaborative networks. Conduct statistical and spatial analyses of ecological and climate datasets. o Implement
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces
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) to determine greenhouse gas and pollutant emissions in cities using atmospheric measurements (MUCCnet: https://atmosphere.ei.tum.de/ ) and in-situ sensor networks in ICOS Cities project (https://www.icos-cp.eu
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national and international research networks as well as pursue independent and collaborative methodological research in Biostatistics. Methodological interest and experience are desired, but not limited
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine