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- Universidade de Vigo
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Field
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, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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self‑delivering superchaotropic covalent hybrids; (ii) performing systematic transport screening to identify key physicochemical parameters and build a cargo‑dependent predictive model for covalent
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house
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carried out in a controlled cooling carousel E. Experimental validation of the numerical model of the heat treatment process for controlled air cooling in a carousel F. Generation of a numerical prediction
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of multimedia datasets (voice, text, etc.). Development of predictive models for cognitive impairment and Parkinson's disease using signal processing and machine learning techniques. Development and debugging
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methodologies that integrate QML with classical models, seeking to improve computational efficiency (runtime and memory usage) and energy consumption without sacrificing predictive accuracy. Specific practical
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models, clinical approaches, AI methods (e.g., NLP), and neuroimaging, with opportunities to participate in network-wide events and international training activities. The position includes implementing a
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CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas) | Spain | 3 months ago
to analyze results and use them to validate current theoretical models and predictive codes. Where to apply Website https://www.ciemat.es/ Requirements Research FieldPhysicsEducation LevelMaster Degree
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Instituto de Ciencias del Patrimonio - Spanish Council for Scientific Research (CSIC) | Spain | 2 months ago
approaches to Rota, i.e LiDAR processing and ground-truthing, and check the capabilities of predictive models for new archaeological sites in tropical environments. In addition to field work in the Pacific