601 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" uni jobs in Germany
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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machine learning for practical implementation. Our group studies natural and designs artificial cognitive systems, researches their relationships and develops methods that enable interactions between them
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | 27 days ago
), is offering a fully funded PhD position in the area of statistical learning, machine learning, and survival analysis applied to large-scale proteomics and multi-omics cohort data. The PhD project
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Future. Discover. Together. The Computer Vision & Graphics group of the Vision & Imaging Technologies (VIT) department is looking for a student assistant in the area of deep learning for scene
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that can be used to compare the arrangements • You select suitable algorithms to evaluate the quality of the PPG signals (machine learning, Python) What we offer We offer you a challenging and varied
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industrial objects. Different acquisition devices are used depending on the object properties and quality requirements. We research modern machine-learning methods for the analysis of an object's optical
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further both professionally and personally in an interdisciplinary setting. Position DWI is looking to fill the position as soon as possible: Research Scientist Machine Learning Engineer - AI-Powered Image
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv at tu-dresden.de or to: TU Dresden, Chair of Machine Learning