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Field
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motivation to gain experience in) quantitative methods and statistical analysis; excellent analytical proficiency and conceptual understanding; strong communication, presentation, collaboration, networking and
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record of scientific productivity is preferable. The successful applicant must have a Ph.D.in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering or related field at the start of
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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openings available. Internal Number: 362548 Required Qualifications: * Ph.D. or Masters with equivalent experience in Computational Biology, Biostatistics, Bioinformatics, Computer Science, Data Science, or
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Administration NAV. Prognostic factors for long-term effects on health and welfare will also be explored. The PhD candidate will be in charge of cleaning, linking and statistically analysing data from the various
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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to the sub-group. Themes might include: the acquisition of productivity of morphological rules; the interaction of linguistic universals and statistical properties of the input in L2/Ln acquisition; linguistic
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MSc in physics, applied mathematics, environmental sciences, computational science or a related field; excellent skills in scientific programming and numerical/statistical analysis of simulated and
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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outstanding students from all over the world to apply for PhD positions in our renowned PhD program. Based in Hamburg, our program offers excellent training and top-level structured supervision in climate