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of machine learning based systems. More in detail, a key requirement for eligibility is provable expertise in the area of prediction of both costs and benefits of retraining or finetuning machine learning
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experience in Machine Learning, Deep Learning and Data Analysis; - The candidate's scientific curriculum and ability to publish scientific papers will be valued; - Suitability of the candidate's training and
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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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representation and data analysis techniques for Web applications and automatic classification techniques, using Machine learning - Development and analysis of case studies. - Integration and reformulation
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as follows: A. Academic curriculum assessment (40%) B. Consolidated knowledge of Computer Science, Data Analysis and Machine Learning (60%) Composition of the Selection Jury: Chair – Pedro José de Melo
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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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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal Application Deadline 17 Mar 2025 - 23:59 (Europe/Lisbon) Type of Contract Not Applicable Job Status Not
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advanced Machine Learning and Deep Learning technologies to automate the identification, classification, and analysis of millions of rock art images. We seek talented and passionate professionals who wish
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expressed in terms of i) work experience in robotics or IoT, ii) experience in the use of machine learning algorithms or artificial intelligence, iii) experience in training and testing deep neural network
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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