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Deutsches Zentrum für Neurodegenerative Erkrankungen | Bonn, Nordrhein Westfalen | Germany | 3 months ago
for an enthusiastic and ambitious early-career researcher with a strong interest in investigating multi-omic and multimodal brain imaging signatures of glymphatic function in the context of aging and cerebral small
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– Water at Catalytic Interfaces Reference code: 956 – 2026/WG 1 Location: Teltow Application deadline: March 4th, 2026 Join the Cluster of Excellence "BlueMat: Water-Driven Materials" (www.tuhh.de/bluemat
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
at both national and international levels, as well as the assessment of their application in spatial solutions, with a special focus on wood, metal, and fiber-based technologies; Design of prototype spatial
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resources, like source code, hardware design, videos, etc. A 2-minute video of yourself explaining why you are a fit for the position You may apply before obtaining your master's degree, but you cannot begin
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data integration data quality data analytics programming languages (SQL, Python, Java) Complementary IT skills: machine learning image recognition sensor technologies robotic technologies
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-economic growth paradigm and including its ever-increasing demand for electricity. In addition, comprehensive future large-scale landscape changes can be anticipated due to a broad range of needs and
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and experiments and documentation of research activities of the of SAFE center. The position as researcher will cover research, dataset development, prototype deployment, and collaboration with
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in order to improve imaging techniques at large radio astronomy facilities. The project will be carried out in the Cosmology and Astroparticle Physics working group (Prof. Schwarz) at the Faculty
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background in Earth Sciences or another appropriate field. You will work on the project “Nanoscale Rock Magnetism and Magnetic Imaging”. Your job This study is part of the ERC Consolidator project SPARK, which
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. MAIAMI aims to establish a paradigm shift in materials science by embedding mechanistic AI agents into physical experimentation cycles—enabling rapid, transparent, and reproducible design of advanced