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clinical data and machine learning algorithms. The main activities include: Data Processing: • Collection of historical patient data (demographics, clinical history, outcomes of interventions). Data cleaning
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clinical data and machine learning algorithms. The main activities include: Support for AI Model Development: • Collaborating on the training of predictive models under the supervision of the scientific team
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. They should be able to thrive in the multidisciplinary culture of INESC MN and fit in projects involving physics, microfabrication, machine learning, mechatronics, etc. and be able to communicate
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, and visualization, time series processing, and machine learning. Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. 3. Financial component - According
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fellowship will be involved in the execution of the following tasks: 1 - Train the metamodels using machine learning techniques; 2 - Validate the machine learning results with experimental results. The aim
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of consultations from texts based on a provided example • Organization of the collected data and its submission to the EquiVet.AI project scholarship holders in the field of Computer Engineering and affiliated with