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(20th of July 2026). Academic background: You must have an honours degree or demonstrated equivalent research expertise in computer science, machine learning, data science, human-computer interaction (HCI
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. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD
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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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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 29 days ago
differential equations # Knowledge of Scientific computing / high-performance computing # Knowledge of Algorithmic modeling and simulation # Fundamentals of machine learning or quantum computing # Programming
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 27 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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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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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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machine learning model to protect their habitats. Omid Yeganeh is currently pursuing a Master of Laws, specialising in public international law and human rights. He has worked on cases before
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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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–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