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Institute of Systems and Robotics - Faculty of Sciences and Technology of the University of Coimbra | Portugal | 21 days ago
Engineering, Computer Engineering, Physical Engineering, Biomedical Engineering, or related areas. Admission requirements: PhD students, or masters enrolled in non-academic degree courses, integrated
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RESEARCH FELLOWSHIP 1 Research Grant, Reference Lab2PT/UIDP-04509/36 Título: Research grant for Phd's students in Archaeology; 1 vacance A call for applications is now open for the attribution of one (1
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project, particularly the Digital Planet activity which supports the analysis of large-scale data from a variety of sources. This data analysis can include using distributed machine learning techniques
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9 May 2024 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 23 May
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4 Jun 2024 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 20 Jun
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, CAN Communication Protocol, Machine Learning, AI, Sensor Networks, Hierarchical Decision and Control Systems with main focus on manufacturing and autonomous systems (1 to 5 points); C. Knowledge
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, development of Machine Learning models for cybersecurity, SPARQL, structured text formats (YAML, Markdown), Linux and Android environments, REST frameworks; - Fluency of both Portuguese and English. Candidates
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11 Apr 2024 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 24
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
: This work aims to develop new machine learning methods for breast diagnosis. Specifically, multiple instance learning (MIL) applied to three different image modalities: (i) mammography, (ii) ultrasound and
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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning