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community analyses Experience with machine learning approaches, including the ability to apply relevant statistical models and predictive analytics to biological datasets, is highly advantageous. Hands
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relevant to advancing our understanding of how the brain encodes and predicts real-world social and behavioural outcomes. By combining high-resolution neuroimaging in controlled settings with wearable
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. Your Role A key pillar of ECOWIND is bridging the gap between remote sensing technology and real-time turbine control. Your focus will be the development of a predictive capability that allows turbines
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]. These systems are characterized by highly nonlinear, anisotropic, and time-dependent responses governed by evolving internal mechanisms and environmental conditions, making their predictive modeling particularly
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to data analysis, feature engineering, model development, evaluation, and documentation, while progressively gaining exposure to production systems, client-facing work, and modern AI practices across
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using methods such as Dynamic Mode Decomposition with control (DMDc). You will also assist in the development of predictive control approaches based on reduced-order models, and contribute to workflow
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interaction scores. Build and deploy machine learning and statistical models for functional genomics predictions, including sgRNA efficiency and drug sensitivity scoring. Collaborate with laboratory members
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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as predictors of plant function and community assembly --- into predictive computer models of terrestrial ecosystems, land-atmosphere interactions, and the Earth System. Field of Science: Earth Science
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(HAC). This role focuses on applying advanced computational and analytical methods—including artificial intelligence, machine learning, deep learning, time-series modeling, and large language models