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that do not confer an academic degree, in the area or area related to that requested in the tender. Preferential factors: Have demonstrable experience in the use of machine learning algorithms applied
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: Fraunhofer Portugal AWAM, Vila Real, Portugal, under the scientific supervision of PhD Mara Silva. Scholarship’s Duration and Regime: The scholarship shall have a duration of 6 months, eventually renewable
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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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Expressions of interest This prestigious Industry PhD project, offered through a collaboration between CSIRO, La Trobe University, and industry partner Rocket Punch Pvt Ltd, seeks to develop
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 3 months ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 23 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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for applications for 2 research grant (Master’s Degree), enrolled in a PhD or a course that does not confer academic degree , within the framework of project La Caixa EndoLEGUME with the reference PD25
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Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and
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for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration
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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