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Field
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-development and refinement of conceptual models; devising management scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief
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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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mathematical background, including expertise in stochastic optimization (e.g. Markov decision theory and dynamic programming) and applied probability (Bayesian statistics). Excellent coding skills (e.g., in Java
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engineering aspects as well as filtering and signal processing. The work is linked to a series of VINNOVA funded projects, REDO, REDO2 and CORD. The purpose of the research is to understand how different
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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