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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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bioinformatics (m/f/d) Are you passionate about bioinformatics and eager to work at the intersection of medicine and academic research? Join our motivated team and contribute to cutting-edge big data analysis in
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science, automation science, or a related field, and convincing expertise in robotic hardware. Experience with machine learning and large language models is highly desirable. Prior experience in a biological setting
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ability to quantify and model these processes remains limited, contributing to uncertainties in global carbon sink estimates. You will analyze data and samples from past and upcoming expeditions to evaluate
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-08187-1 Your Profile: Master and PhD in biology, genomics or bioinformatics Strong background in data science or machine learning (deep learning, statistical modeling, or large-scale data analysis a plus
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hearing loss. However, current neural devices are large, complex, and invasive, and are therefore used by only a fraction of people who could benefit from them. The goal of NANeurO is to design new
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invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
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) is an advantage Experience in software tools such as Origin, Matlab, Python/Matplotlib or similar programs for data processing and evaluation. Knowledge of a relevant programming language complements
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improve the pellet injector on W7‑X Write/maintain safety and quality relevant documentations + specifications Conduct a scientific project within the framework of a large experiment Develop and realize
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sequencing, large-scale genomic, transcriptomic, proteomic, metabolomic, and phenotypic data) using cutting-edge technologies, such as machine learning You will perform transcriptomic and epigenetic analysis