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components. This includes statistical analysis of faba bean growth profiles under different management and environmental conditions. The aim of the PhD project is to develop a methodology for detecting stress
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with a wide range of data formats and engaging with data experts and database managers. The second major focus is advanced data analysis and statistical modeling to identify patterns in fish distribution
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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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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, particularly GBS, continuous-variable QC Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical
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of population changes and formal demography are an advantage. The candidate is expected to apply innovative analytical and/or statistical methods to improve the estimation of population changes at a subnational
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understanding of the process, statistics and data analytics shall be applied to link the different conditions to the likelihood of microcracks occurring. The severity of microcracks may also be studied in
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advanced statistical and epidemiological analyses. Complete PhD courses (approx. 30 ECTS credits). Analyze data from national health registers and surveys. Write and publish scientific articles in peer
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extraction techniques. Strong knowledge of protein science and its application in food systems. Proficiency in analytical techniques relevant to food and protein characterization. Skills in statistical
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Laboratory techniques for sample preparation Analysis of mass spectrometric datasets and statistical calculations Contribute to in vitro analyses Contribute to carrying out animal experiments Contribute