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knowledge and advanced transfer learning techniques. The methodology incorporates fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent
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these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar knowledge and advanced transfer learning techniques. The methodology incorporates
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). Candidates must meet the following requirements: - PhD in Computer Science; - Experience in research on the use of AI to recommend code refactoring opportunities; - Experience with data analysis and data
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learning and community engagement in conservation. Requirements: PhD completed; fluency in English; experience with qualitative methods; experience with and availability for fieldwork, in accordance with
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techniques for the development of wearable devices capable of detecting plant health markers and analytes of agricultural interest. Candidates must hold a PhD degree in Chemistry, Materials Science, or related
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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate
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topics associated with infrared physics, such as celestial holography and related subjects. Applicants should hold a PhD in Physics and have a strong background in scattering amplitudes in gauge theories
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of the email should be “PostDoc CBD photometry”. Applicants should be close to completing their PhD or have obtained their doctoral degree within the last seven years. Shortlisted candidates will be
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projects with tangible outputs; programming skills; operation and planning of electric power distribution networks; and database management. Recently graduated PhD holders are preferred. Application Process
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(LNBio). We are looking for candidates who have: • A PhD in Molecular Biology, Immunology, Microbiology, Biotechnology, or related fields; • Strong experience in molecular biology; • Hands-on expertise