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PhD fellowship in conservation science - developing and validating indicators of ecosystem integrity
conservation science. Applicants can have a background from biology, ecology, or a closely-related fields. Applicants should hold a MSc degree or equivalent. As criteria for assessment of your qualifications
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and using mathematical and statistical models on marine resources. The scholarship is part of a project financed by the EHFAF program. The project will be carried out at DTU Aqua in collaboration
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include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale Letters of recommendation
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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 reproducibility
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scientific results in peer-reviewed scientific articles. Attend conferences to present your research and expand your academic network. Contribute to section work tasks including occasional teaching and MSc
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in English in one PDF file. The file must include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description
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/bioengineering (or a similar degree with an academic level equivalent to a two-year master's degree) and have demonstrated experience in the following areas: Numerical methods Statistics Computational biology
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trials Accuracy and patience for laboratory work Experience with UPLC and Western Blotting Analytical skills and experience in statistical analysis Strong written and oral communication skills in English
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are looking for excellent applicants with MSc background on Economics, Behavioural Modelling, Applied Mathematics or Quantitative Environmental Modelling with the interest and ambition to pursue PhD studies in
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collection. Simulate a multi-scale, multi-sector framework with features generated during the fundamental scientific experiments. Utilizing advanced statistical and computational methods to analyze and