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Field
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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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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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Willingness to learn and interested in interdisciplinary, applied research Our Benefits Attractive and modern working environment with excellent infrastructure and state-of-the-art scientific equipment
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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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Responsibilities Conduct research in computational methods for environmental and engineering applications. Develop and analyze numerical algorithms, reduced-order models, and machine-learning-enhanced simulation
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | 3 months ago
. Experience in machine learning algorithms and tools. Experience in microscopy and image analysis. Effective collaboration in interdisciplinary teams. Strong analytical reasoning and persistence in experimental
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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contribute Completed an excellent doctorate in computer science Proven track record of excellent publications Expertise in cryptography and/or machine learning Leadership capabilities and ability to mentor
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and