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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The successful candidate will develop signal and image processing techniques, as well as apply machine learning
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: Experience in research projects, and writing of scientific papers. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection
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new approach based on physically inspired hybrid machine learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION
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TRAINING: - extend the knowledge of the state of the art in computer vision and machine learning for cancer characterization; - identify and select the appropriate methods for the study in question
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. Preference factors: Experience in research projects. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and
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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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. The programme will involve implementing simulation models that incorporate centralized protection approaches, combined with optimization tools and machine learning techniques to enhance protection performance
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learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering The awarding of the fellowship is dependent on the applicants' enrolment in
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The objectives for this grant are as follows:; - Research and develop machine learning algorithms for the processing of gastric
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: Experience in research projects, and writing of scientific papers. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection