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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning
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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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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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: simulation and risk modelling using advanced statistical and machine learning based methods. strategic portfolio management and dependency structure modelling for financial assets. effects of climate change
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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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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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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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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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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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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