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, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently and collaboratively Effective communication skills and an interest in contributing to a
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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Supervisors: https://www.fz-juelich.de/judocs 30 Days of annual leave and flexible working arrangements, including partial remote work Targeted services for international employees, e.g. through our
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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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ground-truth synchronous patterns under varying conditions to enable systematic validation across relevant scenarios. All software arising from this work including improved analysis pipelines and
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sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
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regression models to isolate task-related submanifolds and their respective role for sensory processing and task performance Analysis of the data to identify higher-order spike correlations and their temporal
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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performance Analysis of the data to identify higher-order spike correlations and their temporal dynamics Are there particular manifold transition points and is their timing related to the occurrence of specific
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Website for additional job details https://www.fz-juelich.de/en/careers/jobs/2025D-177 Work Location(s) Number of offers available1Company/InstituteForschungszentrum JülichCountryGermanyCityJülichGeofield