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under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4 — Submission of the application: 2.4.1
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contract Category (A,B or C) : A Contract/project period : Expected date of employment : March/April 2026 Proportion of work : 100% Desired level of education : PhD (Doctorate
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
resistant and susceptible plants. Develop an AI-assisted image-based phenotyping pipeline to automatically quantify aphid performances on the plants. This software will automatically count for aphids and
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resistant and susceptible plants. Develop an AI-assisted image-based phenotyping pipeline to automatically quantify aphid performances on the plants. This software will automatically count for aphids and
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and software system design, set up validation experiments, and publish the obtained results in high-rated conferences and journals. You contribute to the writing of project proposals and guide Master's
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Department of Chemical and Biological Engineering Posting Number P260025 Posting Link https://www.ubjobs.buffalo.edu/postings/61152 Employer State Position Type UUP Professional Professional Appointment Term
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intensive and has a strong tradition of research-led teaching. The School offers undergraduate majors in computer science, information systems, data science and software development within its flagship
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and scientific exploitation of state-of-the-art, ground-based gamma-ray telescopes. As a key member of the MAGIC and CTAO-LST collaborations, the group develops critical hardware, software, and data
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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Simulations (INMA, Zaragoza) Build and maintain software infrastructure for modeling quantum systems with machine learning tools. Investigate neural-network-based representations of many-body quantum states