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equations Graph theory, network dynamics, reaction diffusion systems High dimensional statistics & regression Global optimization Mathematics of deep learning, neural nets and compressed sensing Requirements
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on marine communities. The major goal of the project is to statistically combine data from the large number of investigated parameters in a way that would allow the derivation of direct or indirect
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variability to conclude about biosphere-climate interactions.This requires the synthesis of data sets from various sources using advanced statistical and data science methods. The project focus on boreal and
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institutions. Tasks You will: develop, improve, extend a deep learning algorithm with modern techniques like Keras use state-of-the-art computer infrastructures on Linux with high-end GPUs develop a statistical
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training. Research experience required in human genetics and cell biology is required for this project. You will work with data from cutting edge sequencing technologies, and therefore a strong statistical
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testing and application of the method to reconstruct a past climate state. In collaboration with machine learning and statistics experts and experts from paleo-climate research, this project aims to develop
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-)mathematics, data science, biology (or related disciplines) Good knowledge in bioinformatic data analyses and programming Basic knowledge in biology/genetics and statistics Very good English language skills
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of phytoplankton experience in handling membrane-inlet mass spectrometers (MIMS) and molecular conduction work skills in the processing, statistical analysis and visualization of data. Further Information
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obtained by mass-spectroscopy experience in microalgae and/or prokaryotic culture work and ecophysiological experiments experience in statistical evaluation of multivariate omics-data sets. Further
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Master's degree in biological or environmental science competence in the English language, both written and spoken basic knowledge in statistical analysis and use of R or similar software experience