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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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WIAS Berlin, Weierstrass Institute for Applied Analysis and Stochastics Position ID: 2306 -PHD [#26752, 25/11] Position Title: Position Location: Berlin, Berlin 10117, Germany [map ] Appl Deadline
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performance will be assessed using finite element analysis and experimental work. Additionally, life cycle assessment will be performed to quantify environmental and economic impacts. This project is intended
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for analysis of the data you create. Your findings will be a vital part of understanding the evolution of cancer drug resistance in cell models and in patients. About you: You’re excited about our research
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on regional-scale modeling and simulation of subsurface flow and geomechanics, including associated numerical and computational methods. Topics may include upscaling, numerical analysis, and the design and
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background in preferably more than one of the following areas: Thermodynamic analysis and simulation of energy technologies/processes/systems Programming tools such as Python, Matlab, Modelica, Pytorch, etc
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animal models and knowledge of interactions between diet, intestinal microbiota and health. Experience with microbiology and gut microbiome analysis is a plus, but not a requirement. Required skills
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compounds in collaboration and support with experienced radiochemists. The candidate will acquire and analyze PET/SPECT imaging data, perform kinetic modeling, and contribute to advanced image analysis
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) Chemical biology/pharmacology Mammalian cell culture Preferred experience: Cell and molecular biology/biochemistry/assay development Proteomics/lipidomics Analytical thinking, data analysis, and critical
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 1 day ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using