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datasets Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer The successful candidates will
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chromatin biochemistry and in vitro reconstitution o bioinformatics workflows (R/Python), statistics, reproducible analysis o third generation sequencing (e.g. Oxford Nanopore) fluent English language
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
skills in computational molecular (bio)physics, statistical mechanics, scientific computing and also a keen interest in interdisciplinary research and collaboration with experimental groups. PhD students
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | about 1 month ago
science, or a related subject Experienced in acoustic propagation modelling, signal processing, and data mining A strong background in programming, statistical analysis, and spatial modelling and mapping
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, probability, and statistical signal processing Familiarity with antenna array signal processing techniques Sound understanding of optimization methods and algorithms Proficiency in Python programming Proven
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 27 days ago
to conduct cutting-edge research at the intersection of demography, sociology, (public) health, and medicine. [Required] Proficiency in at least one statistical software package, such as R or Stata. [Desirable
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programming language such as C or Fortran Strong expertise in data analyses and statistics Experience in ecological or environmental modelling Documented ability to publish scientific papers in international
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to characterize seasonal krill flux in the region Your Profile PhD in biology, marine biology, fishery management and conservation, or related fields. Strong quantitative skills (statistics, modelling) and
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM