51 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at INESC TEC
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this context. Already enrolled in a PhD programme. Minimum requirements: Knowledge of mathematics, machine learning, proficiency in programming languages including Python, C/C++. Experience with machine learning
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: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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Requirements Academic qualifications: -. Minimum profile: Experience in Computer Vision and machine learning. Preference factors: Experience in research projects, and writing of scientific papers. Research
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, and writing of scientific papers. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding
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, and writing of scientific papers. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding
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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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: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC
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networks; - inclusion of links to public websites authored by the candidate (portfolio) in the Motivation Letter or Curriculum Vitae; - experience customizing plugins for dynamic content update in Wordpress
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; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Identify state-of-the-art Vision-Language Models for image captioning; - Benchmark the models in occlusion scenarios; - Cooperate in writing