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Field
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Description The University of Wuppertal is a dynamic, networked and research-oriented campus university. Collectively, more than 25,000 researchers, academic staff and students face the challenges
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Network "Natural Traces", Horizon Europe Marie Skłodowska-Curie Actions, European Commission. Secondment: University of Potsdam, Germany and/or Anath Genomic Consultants AB in 3rd year of PhD, up to 3
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 19 days ago
performance and experience relevant to the scientific area and the project work-plan, including participation in R&D projects (especially the areas valued in point 2 of this notice) and international networking
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workflows between field acquisition teams, data center staff, and research collaborators to ensure seamless data transfer and integration. -Support daily operations of servers, networks, storage, and cloud
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supported in developing cutting edge financial tools as well as their software implementation. Building up personal network: You will have the chance to meet top researchers of the field here and during
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-healthcare technologies•Security and privacy in computing systems and IoT•Intelligent wireless communications and networks•Systems-on-chip, FPGA/microcontroller design, real-time systems•Biomedical
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-Brain inspired Neuromorphic Nanophotonics (InsectNeuroNano). The long-term vision of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and
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(at least in Python and C++); ideally strong hands-on experience with ROS2. Experience in AI development, especially with neural networks. Experience with standard software development tools (Git, CI/CD, IDEs
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Archipelago, integrating it into the global e-CALLISTO network. The project will deploy new-generation spectrometers and antennas to fill the observational gap over the North Atlantic, enhancing global space
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural