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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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related areas, with a special emphasis in the areas of Artificial Intelligence, Machine Learning and data models, in addition to app development; - If the candidate holds a higher education degree obtained
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond
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Are you interested in understanding and modeling human capabilities to shape the future of autonomous systems? We are looking for a motivated PhD student to join an exciting research project focused