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Field
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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(2024). 2. Multiresonant Grating to replace Transparent Conductive Oxide Electrode for bias selected filtering of infrared photoresponse, Tung H. Dang, M. Cavallo, A. Khalili, C. Dabard, E. Bossavit, H
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not limited to, QC filtering, enrichment profiling, sequence content comparison, data tracing, and graphical representation. - Large-scale in vitro production and characterization of mRNA. The most
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in collaboration with international research and industrial partners. The position requires software development within the topics of navigation, sensor fusion, Kalman filtering and gravity field
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.). Experience implementing signal processing techniques, including IIR filters, transfer functions, spectral analysis, etc. Experience working with benchtop instrumentation, including power supplies
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational