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processes. Your responsibilities will include: Conducting high-quality research on the suitability of available methods to model metal-ligand complexes in water, with a focus on machine learning techniques
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following use cases: • The construction of a machine learning pipeline that allows the conversion of Course Unit Sheets (CUS) into a data structure based on the European Learning Model (ELM). • Integration
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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work on adapting or developing marine foundation models. Self-supervised learning and active learning are also possible research topics. You can also focus on challenges related to modelling physics
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processing, neuromorphic engineering, or a closely related field. A solid background in machine learning is expected, with interest or experience in spiking neural networks, temporal modeling, or bio-inspired
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deliverables on a diverse array of projects while supporting Center faculty, staff, and student researchers. Broadly, the Research Associate will lead or support the following tasks: Data analysis, modeling
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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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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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models, and data processes. This role requires advanced data science and machine learning expertise, proficiency with Python ML libraries, strong SQL programming skills, experience with data pipeline
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of a call for awarding a research fellowship (RF) in the scope of the research project AQUALEARN – Machine learning-based digital twins for real time anomaly detection in water supply systems. 3