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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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integration, metadata harmonization, preprocessing, and quality control of large public sequencing datasets Implement and benchmark machine-learning models for predicting biological and ecological metadata from
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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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by The Kempe Foundations. Project description Machine learning and artificial intelligence have had a major impact on medical image analysis in recent years. While CT and MRI provide highly
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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want to grow into strong engineers and researchers in either: data & systems for high-frequency pipelines, and/or machine learning models, infrastructure and experimentation Strong fundamentals
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. Connections working at New York University More Jobs from This Employer https://main.hercjobs.org/jobs/22186757/junior-research-scientist-in-the-division-of-engineering-x28-electrical-and-computer-engineering