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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 days ago
are the following: This grant aims to benchmark deep learning-based CAD-Free RGB(D) pose estimation methods for spacecraft applications (2 grants) This grant aims to develop robust MPC strategies based on two player
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or related field;* Solid knowledge in machine vision and deep learning (e.g., TensorFlow, PyTorch, OpenCV); Experience in Python programming (focus on libraries for data analysis and AI); Previous
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C644914747-0000023 funded by funded by Recovery and Resilience Plan (RRP) https://recuperarportugal.gov.pt/ and Next Generation EU European Funds Application submission from 20-Feb to 06-Mar-2026 WHO WE
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. Previous experience in machine learning applied to dynamic systems or orbital
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acquire advanced expertise in navigation models, spatial representation, object representation, and relational knowledge representation, as well as in planning algorithms based on probabilistic models and
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of computational thinking and didactic knowledge in mathematics among future teachers of the Primary School; Prepare and submit scientific articles. Where to apply Website https://www.ipc.pt/bupc/concursos/form
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to provide the supporting documents by the grant contracting stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. 4. Work Plan The work
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 2 months ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate