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Field
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learning methods to digital pathology Development of deep learning algorithms for the computational analysis of whole-slide images. The objective is to identify relevant biological features and to perform
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with the installation and operation of lab equipment is considered a plus; Familiarity with neuromorphic computing concepts and device modeling is also considered a plus; PhD in Physics, Materials
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enrolled in a PhD program - art. 6º, n.4 c) https://diariodarepublica.pt/dr/detalhe/regulamento/950-2019-127238533 https://files.dre.pt/2s/2019/12/241000000/0009100105.pdf subject to suitable performance
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4 Sep 2025 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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35 Offer Starting Date 17 Sep 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
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Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description Job Reference: Ref.50.25.443 Employer: International Iberian Nanotechnology
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the job funded through the EU Research Framework Programme? Other EU programme Reference Number UIDP/50009/2025 Is the Job related to staff position within a Research Infrastructure? No Offer
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.; ●Research and develop novel generalizable deep learning computer vision algorithms for EGGIM estimation; ●Publish results in high impact journals and conferences; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME
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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 17 days ago
: Familiarization with hardware integration: training in the assembly, configuration, and operation of hardware components (sensors, computing units, communication modules) to understand their role in forest data
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of the research project “PAINLESSFISH - Improving fish welfare in aquaculture with natural analgesics”, no. 15468, operation code COMPETE2030-FEDER-00915000”, funded by the SACCCT Programme – Scientific Research