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Field
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analysis, machine learning, mathematics of data scienceMathematical modeling and manifold learning of high-dimensional data geometry, with theoretical foundations and applications in evolutionary biology and
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numerical simulations and analytical calculations, (iii) data analysis by programming, e.g. python and matlab, (iv) help and guide of Ph.D students in the team, (v) writing research reports, publishing
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model the candidate will perform a set of numerical models of an orogen testing the impact of the inherited extensional deformation on (i) the sediment routing systems and associated sediment volumes
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, with the possibility of renewal, in the field of Analysis and Partial Differential Equations (PDEs). Job description - The successful candidate will collaborate on research projects related
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Principal Investigator of the OrDOTime project, and a collaborator on the project. Literature review, design of behavioural experiments, data collection, data analysis, writing scientific articles
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-Chem simulations for modeling aerosols and clouds in the Arctic region. - Analysis of the interactions between aerosols, clouds, and climate using data from the simulations. - Validation of the models
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techniques. ● Proficiency in instrument control, data acquisition, and data analysis using tools such as LabView, Python or MATLAB. ● A record of peer-reviewed publications and demonstrated ability
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oceanography, numerical modeling, acoustic propagation, and scientific computing. The postdoctoral researcher will develop, apply, evaluate, and integrate uncertainty modelling into the predictions
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to scientific computing and other scientific domains. Candidates with strong backgrounds in stochastic analysis, numerical analysis, sampling methods, optimization, scientific computing, or related areas
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for students, line-manage Research Assistants and D.Phil. students, mentor on methods, analysis and career development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g