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Field
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contribute to the design and development of Machine Vision approaches for the quantitative analysis and phenotyping of agricultural systems: Training/Development of computational models for the quantitative
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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Universidade Lusófona´s Research Center for Digital Human-Environment Interaction Lab | Portugal | 2 days ago
Python Machine Learning Libraries (PyTorch, Keras, etc.); statistical and machine Learning expertise and supervised and unsupervised methodologies; Valuable Bonus Skills include: experience with Unity3D
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learning techniques, developing attention-based transformer models that will directly classify events from the low-level data, rather than high-level objects, and which may ultimately include higher-level
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AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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qualities required for such purposes. Digital terrain models will be used to efficiently map cultural remains under forest canopies. The second project (Mapping Natural Forests in Norway) focuses on mapping
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accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too
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will include boosted final states. The analysis strategy is strongly focused on advanced machine learning techniques, developing attention-based transformer models that will directly classify events from