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Field
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Information , Data Visualization , Deep Learning , High dimensional Data , Large Language Models , Large Scale Optimization , Machine Learning , Natural Sciences , Public Interest Tech Computational Biology
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reporting standards * Familiarity with deep learning technologies (e.g., CNNs, transformers) * Experience with Python and ML ecosystem skills (e.g., PyTorch, scikit-learn, etc.). * Skilled in writing and able
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a valuable opportunity to collaborate with and learn from other scholars and experts within the CIL Oceans Law and Policy Team, in a dynamic and supportive research environment. CIL supports both
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cross encoder architectures and retrieval augmented generation. Strong programming skills in Python and deep learning frameworks, familiarity with MLOps practices, data governance for sensitive health
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characterization of integrated memristor devices and systems. • Develop algorithms and system-level integration strategies to harness the capabilities of AI accelerator for machine learning and deep learning
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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learning and statistics, and who are eager to contribute to impactful methods for generating private and fair synthetic data with good utility. This project involves development of deep learning based
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sequence or structure analysis, machine-learning method development, and modern software engineering practices. They should be proficient in Python and Linux-based development, familiar with deep learning
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or Machine Learning). The master thesis must be included in the application. Documented proficiency in English, please see requirements below. Requirements for proficiency in English This position requires