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Field
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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incremental optimization. We seek researchers to develop next-generation machine learning methods that fundamentally rethink how large-scale AI systems are trained, fine-tuned, and deployed. Our focus is on
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some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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genome encodes gene expression levels. You will undertake large scale data generation from primary human samples using a method recently pioneered by the host laboratory (Hua et al., Nature 2021 https
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international
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of Spanish (not required but valued for teaching and policy dissemination in Spain). Experience with AI-based research workflows, machine learning techniques applied to financial data, or modern