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of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks - Documented several years of experience in systems development with Python and
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, Environmental Science, Remote Sensing, or related field Experience in atmospheric modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record Where to apply
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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understanding of deep learning concepts, various network architectures, and practical model development. Experience with model deployment, experimentation, or reproducible machine learning workflows. Experience
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
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partnership between academia and industry to drive research and development forward. Project description This project aims to develop unsupervised machine learning methods for extracting dynamical models
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Qualifications: PhD in computational genomics Experience with: • Computational and bioinformatics • Machine learning and statistical modeling • Programming and data infrastructure • Experimental and Field Methods