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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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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
apply machine learning/AI methods for ecological analyses Expedition experience Further Information The AWI is characterized by The AWI is characterized by our scientific success - excellent research
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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omics, environmental, and chemical data, using machine learning and explainable AI. Depending on your background, interests, and evolving project needs, your work may focus on one of these areas or bridge
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Your Job: You will develop impactful machine learning techniques to deal with complex quantum states. Possible research directions and tasks include: Method development to advance neural quantum
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its detailed analysis through Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap
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Your Job: In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage