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environment flexible working hours – mobile working options – assistance with child care and care for family members („audit berufundfamilie“) – employee ID card with free admission to the Senckenberg museums
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functionalities (GUI and web-service) Participate in field work organization, sampling plan establishment and in-situ data acquisition Your Profile PhD in environmental sciences or computer science, with a proven
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stakeholder materials; no academic research output required Manage and analyze financial risk data for technical solutions supporting the identification of ESG risks Support the project’s deliverables and
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the study of the impact of digital and computational pathology on clinical workflows and patient care. Our lab is located in the heart of Munich at the TUM Klinikum rechts der Isar (MRI), Institute
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. Topic / Area: The positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in
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management. Our group combines empirical work (with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets
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Institute (https://www.mdsi.tum.de/). The Position Plan, develop and test novel computational models for the analysis of digital pathology image data. Collaborate with pathologists and other domain experts
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fluids, design novel numerical simulation and optimal control schemes, and provide new means for risk management. This project mainly focuses on the analysis and optimal control of the underlying SPDE and
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the understanding of the trade-offs between production, mitigation and conservation in livestock-based systems, and to identify innovative mechanisms for landscape-level management. Our group combines empirical work
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assurance/quality control and data management, and provide a unique opportunity to address a broad variety of fundamental and applied ecological questions, including those related to community ecology