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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 2 months ago
Job Offer PhD Studentship | Social Statistics with focus on Digital and Computational Demography About the Project We are pleased to invite applications to a 3.5-year University of Manchester
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. Your tasks identify relevant satellite datasets of irrigated areas, as well as of hydrometeorological, land surface and vegetation variables apply modern statistical methods on these datasets to detect
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statistical data evaluation, creation of scientific programme codes using common software packages (MATLAB, Python, R) Simulation skills with e.g. molecular dynamics and alpha fold Knowledge
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, carbohydrate-active enzymes, polymer chemistry, spectroscopic methods, material science, 3D-printing, statistical physics and molecular dynamic simulations, formulation technologies. Excellent communication
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and written English and the will to acquire a certain working
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microbiome, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights into these complex interactions. As part of this position, you will have the
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills
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Diploma or Master´s degree in geosciences, environmental sciences, physics, atmospheric sciences, or related disciplines Comfortable with statistics, mathematics, and knowledge in the carbon cycle Curious
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific