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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc: Networked Systems Abstractions Lab (LASeR) Mission The EPFL Networked Systems
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hyperspectral imaging is necessary. Proficiency in Python programming, particularly with Python libraries such as Spectral Python (spectral), NumPy, SciPy, and OpenCV and familiarity with at least one engineering
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selectivity between materials and minimize plasma-induced damage, will be evaluated as part of this study. To achieve this objective, it will be necessary to understand the etching mechanisms of the different
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individual differences in stress vulnerability and resilience with a specific focus on how early life adversity shapes hippocampal and corticolimbic circuit function to influence fear behavior and cognitive
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aerosols. - Visualization and post-processing tools: Proficiency in visualization tools (e.g., Python) and data post-processing tools to analyze simulation results and generate graphs, maps, and diagnostics
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ultrasound sensing and hyperspectral imaging is necessary. Proficiency in Python programming, particularly with Python libraries such as Spectral Python (spectral), NumPy, SciPy, and OpenCV and familiarity
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Divergence (MIND) approach for estimating structural similarity networks, which enables robust structural brain networks to be derived from T1-weighted images alone and has already been shown to be sensitive
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. PostDoctoral Position : Image-Based Drug Screening Against Bacterial Pathogens Mission A
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. The candidate will lead the C++ / Unreal Engine software engineering efforts, ensuring sub-millisecond data synchronization between simulation frameworks and external Python-based AI models hosted at partner
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microbiology, bacterial genetics, CRISPRi approaches, and bioinformatics analyses to identify the molecular and genetic determinants underlying strain-to-strain differences in stress responses. In particular