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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
fields Experience with image analysis, or computer vision Good knowledge of basic machine learning techniques, such as variational autoencoder Good presentation and writing skills Proactive, independent
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Strong foundations in Machine Learning and Deep Learning Excellent Python programming skills Experience with PyTorch
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-based methodologies for protein structure prediction and cofolding Machine learning guided virtual screening Taking on organizational tasks and writing grant applications Your Profile The ideal
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, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the above-mentioned research areas collaboration in national and international research projects, possibly
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, for event reconstruction and classification, including potentially machine learning/AI Interpretation in suitable theoretical models Contribution to software activities that are required for wider use by DESY
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The Cluster of Excellence Physics of Life" (PoL, https://physics-of-life.tu-dresden.de/en ) is an interdisciplinary research center at TUD Dresden University of Technology dedicated to quantitative
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for working in a competitive interdisciplinary research team are essential. Potential scientific projects will use computational neuroscience methods such as dimensionality reduction, machine learning
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency