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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | about 4 hours ago
, 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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water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
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skills are required for the job: - Computational modeling of molecular crystals. - Computational and theoretical chemistry. - Crystal structure prediction. - Familiarity with the Linux operating system
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, and c) predicting new phenomena and discovering improved materials for applications. My efforts in this area use a variety of modeling approaches to answer questions on materials systems of interest
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anticipating crises. Current landslide prediction models, based mainly on rainfall thresholds, become ineffective in the presence of snow cover. Snow acts as a temporary reservoir, storing precipitation before
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better quality of life for patients and caregivers, and lower healthcare costs. The target is to define new intelligent computational models by reshaping risk prediction, diagnosis, and management of a
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experimental and computational datasets. The overarching objective of this work is to establish predictive, patient-specific models capable of forecasting clinical outcomes in breast reconstruction, thereby
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experimental design. Deep expertise in predictive modeling, classical ML algorithms (e.g., decision trees, gradient boosting), large language models (LLMs), generative AI, MLOps, and AutoML using frameworks like
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of mold free shelf-life predictive models, determining the number of variables as well that need to be recorded to be able to train the model; (ii) design and development of a model to predict mold growth
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including forecasting models to predict the expected distribution of pests on the field to landscape scale. The research is expected to make pest forecasts and link them to the existing expertise in crop