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trophoblast cells in a small animal model. The project is part of the new Collaborative Research Center (SFB 1713) “Maternal Immune Activation: Causes and Consequences”. We are seeking a highly motivated and
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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viability using multiple detection techniques (FACS, microscope, spectrophotometer). Collaboration on the analysis of created bacteria in Zebrafish models. Analyse data, contribute to scientific publications
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plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: Determine
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approaches across a range of model organisms to understand how and why we age. As a PhD candidate at FLI, you’ll be part of an international and interdisciplinary environment where basic science meets
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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contributes to the improvement of climate prediction models. The Atmospheric Chemistry and Atmospheric Microphysics departments are looking for a committed doctoral student to carry out this project. You can
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data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using programming languages like R, and/or Python. You have excellent
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microscopy. Root architectural phenotyping. Plant and root physiological measurements to determine the function of root phenotypes. Functional-structural plant modeling to determine the benefit of root
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for stochastic models or optimal control is desired. What we offer: WIAS Berlin is a premier research institution known for its strength in optimization, optimal control, dynamical systems, and applied mathematics