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new standards in computational metabolomics – facilitating biomarker discovery, advancing personalized health monitoring, and improving clinical decision-making. The work will be carried out under close
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are necessary to complete the task. If you hold a diploma or Master's degree in Computer Science or Engineering, possess a sound knowledge of applied informatics and want to join a highly motivated research group
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in Earth Observation develops innovative methods for information extraction from remote sensing data in close cooperation with the Department EO Data Science of the Remote Sensing Technology Institute
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and distributed systems. Applicants need to have a strong background and interest in algorithms and/or combinatorics. You ideally should have an MSc degree in Computer science with a focus on algorithms
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world. The workflow spans from analytical chemistry to material science and engineering. There is no need for previous knowledge in the described fields but a strong motivation to learn and push the boundaries of our
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network of collaborators at the University of Queensland, across Europe (e.g. Lancaster University, The University of Manchester, Wageningen University, University Giessen, Göttingen University, GFZ Potsdam
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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extraction from remote sensing data in close cooperation with the Department EO Data Science of the Remote Sensing Technology Institute of the German Aerospace Center (DLR). For this international, exciting
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21.10.2021, Wissenschaftliches Personal PhD Position at TUM Department of Science, Technology & Society (65% for 3 years) within a DFG-funded research project led by Dr. Susanne Koch and affiliated
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. Expected qualification: High motivation and commitment to scientific excellence Above-average Master Degree / Diploma in engineering (chemical engineering, biochemical engineering, or related engineering