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of computer vision models is comfortable working in multidisciplinary research environments, where methods are applied to biological or real-world problems Qualifications The applicant must: hold a PhD in a
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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-based vision (event cameras), we are recruiting a research and development engineer within our Computer Science (INFO) and Mathematical and Electrical Engineering (MEE) departments. In collaboration with
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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: https://www.ncl.ac.uk/medical-sciences/research/institutes/ . Qualifications Close to completion of PhD in research area required (Research Assistant). PhD in chemistry, biochemistry, or a related
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Health based at The Walter and Eliza Hall Institute in Melbourne, Australia. You will be part of a multi-disciplinary team that has an ambitious long term research program to decipher what factors result
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) Established Researcher (R3) Application Deadline 20 Feb 2026 - 00:00 (UTC) Country Morocco Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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, and managing all professional and technical staff; and supervising students. Qualifications: Mandatory: PhD in Computer Science or similar field; At least one (1) publications as first/second author in
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring