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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
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, of 28 of August, and also the provisions of article nº 9 of the Scientific Research Grant Regulations of the University of Aveiro. 5. Work Plan: Intelligent and modular controller with machine learning
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the port area; Good technical and scientific knowledge in mobile development, and/or web programming, and/or human-computer interaction (UX), and/or application of machine learning; Sense of responsibility
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of article nº 9 of the Scientific Research Grant Regulations of the University of Aveiro. 5. Work Plan: Intelligent, modular battery with machine learning algorithms. The aim is to develop a high-performance
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to secretaria.mia@uc.pt . Reference of the expression of interest: MIA-AR-2025-03 Scientific area: Data Science and Machine Learning Work plan/objectives: The “Continuous Learning Lab”, led by Dr José Sousa
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FARM-ID - Associação da Faculdade de Farmácia para a Investigação e Desenvolvimento | Portugal | 17 days ago
Doctoral Candidate position within the project HORIZON-MSCA-2024-DN-01 – GA 101226058, entitled “Low Data Machine Learning for Sustainable Chemical Sciences”, funded by the European Commission. This project
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project
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Applications — Award of a Research Initiation Grant (BII) Call Reference: BII_1_DEEC A call is hereby opened for the award of one (1) Research Initiation Grant (BII) under the scope of the Learning Environments
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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved