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Field
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for the sale of selected agricultural commodities of regional producers Supervisor: doc. RNDr. Dana Országhová, CSc. Workplace: Institute of Statistics, Operations Research and Mathematics Field of study
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data. You will work in an interdisciplinary research environment, where there is expertise in occupational and environmental medicine, epidemiological-, statistical-, and dispersion modelling, and also
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with a community of ambitious researchers from the fields of machine learning, statistics, logic, language technology, and ethics. Integreat, the Norwegian centre for knowledge-driven machine learning
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models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent advancements have improved its economic interpretability and
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be achieved using artificial intelligence (AI), machine learning and statistical methods. The DC will collaborate closely with other sites and fellows within the ETN to achieve the following objectives
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dynamics) processes influencing energetic particle precipitation into the atmosphere statistical methods for handling complex data Experience from data analysis using scientific programming, e.g., Matlab
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experimental methods and techniques to study and characterize sound and vibration on the millimetre and sub-millimetre scale as well as statistical methods to evaluate metrological aspects such as
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analysis, programming and statistical evaluation Implementation of established and development of new workflows for biomarker identification from omics datasets (proteomics, metabolomics, Raman) Combining
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the identification of sustainable foods Supervisor: doc. Ing. Renáta Benda Prokeinová, PhD. Workplace: Institute of Statistics, Operations Research and Mathematics Field of study: Economics and Management Study
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International Centre for Translational Eye Research / Institute of Physical Chemistry, Polish Academy of Sciences | Poland | 7 days ago
applications Supervision over students Specific Requirements M.Sc. degree in Math, Statistics, Physics, Computer Science or related quantitative discipline Background in statistics, machine and/or deep learning