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-sampling data. Furthermore, the position holder will play a central role in creating high-quality training datasets (seagrass maps) to support artificial intelligence (AI) algorithms used in related projects
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Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data
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mechanisms will be investigated in healthy and clinical populations (i. e. psychiatric or endocrine) both in experimental and cohort data. This highly interdisciplinary research requires a systematic
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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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for the above-mentioned position from women. Preference will be given to equally-qualified candidates with disabilities. For further information, please contact Prof. Dr. Stefan Schreieder (contact details below
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. Involvement in immunology projects with single-cell transcriptomic data analysis. Bioinformatician/Computational Biologist/Systems Immunologist (f/div/m) for two years initially with a possibility of extension
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advanced wet-lab experience in molecular biology and in reverse genetic approaches. • You are familiar with FAIR data handling and in silico data analysis. • You work precisely and reliable. YOU FIT TO US
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18.10.2022, Wissenschaftliches Personal The lab for Artificial Intelligence in Medical Imaging (www.ai-med.de) is looking for a Post-Doc. The task will be the multi-modal modeling of medical data
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Researcher (m/f/d) (salary group TV-L E13, 100 %, 22 months) Preferred start date is as soon as possible. Duties and functions: Intuitive information processing has clear advantages for human cognition but
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current