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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
of computer networks, namely the various protocol layers of the OSI model; - Knowledge of IEEE 802.11 networks and narrowband HF links.; ; Research Topic #3 – Video Transmission using Semantic Communications
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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scenarios., Experience with observability tools, particularly OpenTelemetry., Solid knowledge and experience in machine learning, deep learning, and large-language models (i.e., ResNet18, ResNet50, AlexNet
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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
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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
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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
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-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: 1. Formulate and validate automated inspection methodologies based on computer vision and AI techniques
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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manipulators capable of adjusting their trajectory and resistance in real time in response to variable external loads. This module should integrate learning algorithms based on artificial intelligence, allowing
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on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Experience in musical audio machine learning frameworks, advanced knowledge in music theory, and