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carbon cycling, with a focus on boreal forest ecosystems Experience with data processing software for eddy-covariance data (EddyPro, REddyProc) Excellent numeric and analytical problem-solving skills
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evaluating carbonation and chlorides being valued; d) knowledge in statistical data analysis, with experience using mixed linear models being valued; e) knowledge in data processing and presentation using
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
chemistry and process engineering. We are looking for talented people to join us. In this project, neural operators are combined with classical numerical methods to enable the efficient approximation and
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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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evaluation of modal-decomposition techniques applied to data from high-fidelity numerical simulations of landing-gear aeroacoustics. The researcher will develop and implement modal-decomposition methods using
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learning analysis of biomedical data and bioscientific programming for projects on neurological diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical
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located in Fayetteville, a welcoming community ranked as one of the best places to live in the U.S. The growing region surrounding Fayetteville is home to numerous Fortune 500 companies and one
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insight provided by the experimenter, using methods such as Proper Orthogonal Decomposition (POD) [8], spectral analysis, or Dynamic Mode Decomposition (DMD) [9]—which are currently being implemented in
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the cultivation, maintenance and monitoring of cotton plants in the greenhouse. Carry out the physical and phenotypic characterisation of modified cotton fibres. Conduct processing, statistical analysis and
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the scientific field of the competition. c) Previous experience in statistical analysis of time series and mixed models. d) Previous experience in processing and analysing physiological data (electrodermal