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Field
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-reviewed publications. Prior experience with biological network analysis and practical application of a variety of machine learning and computer vision techniques is preferred. The successful candidate will
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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data from both tissue and single cells, for improved understanding of Alzheimer progression. Experience in brain disorders, machine learning and deep learning will be a plus. Interested candidates should
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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translational research involving multimodal neuroimaging analyses, statistics, machine learning, and/or glucose metabolism are preferred. Education and Experience Requirements: PhD in neuroscience
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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programming; experience in Python for machine learning and/or digital image processing is preferred. Strong communication skills, both in formal written reports/manuscripts and oral presentations. Language
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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. The appointee will primarily conduct research applying advanced machine learning/AI (including techniques like deep learning) to analyze complex biological and clinical data (e.g., single-cell multi-omics
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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore