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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | about 14 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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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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solutions, focusing on building robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 7 days ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
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DEVCOM Analysis Center. Topics of particular interest include: 1. Development of novel machine learning and AI models, as well as the adaptation of existing approaches for AI-enabled decision aid systems
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing