28 parallel-processing-bioinformatics uni jobs at Free University of Berlin in Germany
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Responsibilities: The successful applicant will cover research and teaching in the field of data-driven material research with a reduced teaching load of two weekly credit hours at Freie Universität Berlin. The professorship includes the opportunity of establishing and leading a newly founded...
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of cloud microphysical processes, with the goal to better understand clouds and their interactions with other Earth system components. Using this framework, we strive for a high degree of process-level
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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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metabolism change, paralleling their locomotion in the gut, (ii) Ascaris suum L3 larvae use different metabolic pathways in distinct body regions, (iii) with Artemisinin derivatives not interfering with
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into high-quality, reusable Python software packages. - Engaging in the full software engineering process, including refactoring, comprehensive testing to ensure code reliability, and creating clear
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a complex and large aquifer system are in the focus of this project. The main goal is to gain a better understanding of transport processes, particularly with regard to future challenges posed by
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and processes your data. FU Berlin cannot guarantee the security of your personal data if you send your application over an unencrypted connection.
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The planetary geodynamics group led by Prof. Dr. Lena Noack uses computational models to characterize planetary processes that impact the long-term evolution of the planetary interior coupled
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(e.g., handling bacterial or fungal cultures) - Basic knowledge of antimicrobial or inhibition assays - Good data handling and data processing skills (e.g., in Excel and GraphPad Prism, Python etc
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of mathematicians, computer scientists, chemists and physicists, we put great emphasis on bridging the gaps between the various disciplines. Our main research areas are: Physics-constrained learning algorithms