24 10-phd-candidates-or-postdoctoral-researchers-in-machine-learning-and-deep-learning Fellowship positions at INESC TEC
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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; experience in deep learning; experience in processing and analyzing biomedical data. Research FieldComputer science » Computer systemsYears of Research ExperienceNone Additional Information Benefits
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TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
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3 Sep 2025 Job Information Organisation/Company INESC TEC Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 8
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AND TRAINING: - survey and analyze the state of the art in emerging wireless networks, including simulation aspects using real data assimilation, Machine Learning, and digital twin approaches
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Education Institutions. Minimum profile: Experience in Computer Vision and machine learning. Preference factors: Experience in research projects, and writing of scientific papers. Research FieldEngineering
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Education Institutions. Preference factors: Experience in research activities Minimum requirements: Knowledge of Computer Vision and Machine Learning 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS
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26 Aug 2025 Job Information Organisation/Company INESC TEC Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 10
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26 Aug 2025 Job Information Organisation/Company INESC TEC Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 8
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requirements: - solid knowledge of the TCP/IP stack and Linux; - proven experience in programming with C++ and/or Python; - research and development experience in the field of computer networks. 5. EVALUATION