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- Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID
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to the stage of contracting the scholarship, and before that, they may be replaced by a declaration of honor. Preferential factors: • Expertise in Machine Learning; • Experience in developing machine learning
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value candidates with interest and/or experience in the following areas: a) Understanding of machine learning techniques, with interest in exploring algorithms such as regression, decision trees, Random
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 11 days ago
. The candidate(s) may also be required to apply data fitting algorithms/machine learning algorithms to link models to biological data from the literature. The project integrates elements from dynamical systems
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on the development of methodologies and techniques of Evolutionary Computation and Machine Learning. V - Initial grant duration: 3 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial
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application of statistical Machine Learning tools. WORK PLAN Collaborate in the following tasks of the project: a) Contribute to the design and development of the Life Cycle Assessment (LCA) system; b) Support
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–5): selection of relevant climatic variables and application of statistical modelling and/or machine learning techniques to predict risk. 3) Preliminary validation of the predictive model using
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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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vision based object detection tools, and reinforcement learning techniques. Additional Information Benefits Monthly Maintenance Allowance: €1,309.64 Funding Entity: Instituto Superior Técnico (IST
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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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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