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supervision: The workplan will be carried out in the Centre of Biological Engineering of University of Minho, located in the University of Minho, Campus of Gualtar, in Braga, under the direction of Doctor Nuno
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: Admission requirements: Student in Electrical Engineering, Informatics Engineering, or similar. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses
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of Biological Engineering of University of Minho, located in the University of Minho, Campus of Gualtar, in Braga, under the direction of Doctor Nuno Miguel Dias Cerca, coordinator of the host entity, and whose
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15 Sep 2025 Job Information Organisation/Company Centro de Engenharia Biológica da Universidade do Minho Department Engenharia Biológica Research Field Biological sciences » Biological engineering
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; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Identify state-of-the-art Vision-Language Models for image captioning; - Benchmark the models in occlusion scenarios; - Cooperate in writing
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of artificial intelligence tools, computational modeling, and advanced imaging for the development of biomaterials – 70%; academic background – 30%. The jury reserves the right not to hire any of the candidates
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
scientific publications within the scope of the work developed; - prepare the research grant activity report.; 4. REQUIRED PROFILE: Admission requirements: Student in Electrical Engineering, Informatics
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 23 days ago
; - Proven experience in cell culture (e.g., iPSC neuronal differentiation), microelectrode array (MEA) recordings, calcium imaging, and programming (e.g., MATLAB, Python); - Be enrolled in a PhD Program or an
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by the European Commission under the Horizon Europe program for the period 2021-2027. 1. GRANT DESCRIPTION Type of grant: Research Grant (BI) General scientific area: ENGINEERING Scientific subarea
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop