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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 17 days ago
models as predictive tools to address questions regarding the response of deep-sea ecosystems to various pressures. A key question addresses the best combination of ML and network analysis to maximize
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual
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the ecosystem as a whole. We are looking for a highly motivated and talented Ph.D. candidate to join our research team, dedicated to exploring the functioning of supply chains and performance
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area: Biology, with a focus on wetland ecology, biodiversity, and ecosystem services Admission requirements: Biology graduates and master's degree holders who are, on the scholarship start date, duly
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analysis, modelling and field experiments, our project will illuminate the way forward for assisted migration (AM) as a pathway to sustain, and to restore in case of degradation, the biodiversity and
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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of an ephemeral resource on nutrient transfer between ecosystems: a case study on carrion in wetlands”. 1.2. Reference: CNS2023-144791 1.3. Funding agency: MICIU/AEI/10.13039/501100011033 1.4. Co-funding agency
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development