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reliable data pipelines that power machine learning models, analytics platforms, and enterprise reporting. They will have responsibility for sourcing, cleaning, validating, and integrating data across
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) are required - Experience in working with Earth system model simulations is required - Experience in machine learning is required - Willingness to travel for work (project meetings, workshops, and research
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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AI and related areas such as large language models (LLMs), prompt engineering, and machine learning. You are proactive about staying current in a rapidly evolving field. Rather than waiting for trends
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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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the responsibility of this role. The ideal candidate would have teaching experience and instructional experience in tech tools and computer programming to support student learning. For more details about UF benefits
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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expertise in machine learning or computational modelling who are eager to advance conceptual innovation toward practical industrial deployment. Qualifications PhD in Computer Science, Machine Learning
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Conocimientos de programación de nivel medio a avanzado (lenguaje preferido: Java). Conocimientos básicos sobre machine learning. Capacidad para redactar artículos científicos de alta calidad (por ejemplo, tesis