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Field
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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processing, artificial intelligence, cognition and deep learning, machine learning, navigation and mapping, autonomous driving, assistive robotics, drones, dynamics and vibration, acoustics, medical imaging
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, Data Science, or related fields. Proven publication record in reputable journals. Strong expertise in deep learning, artificial intelligence, and machine learning methodologies, with high competency in
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem
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in deep learning approaches and also experimental experience. Good communication and writing skills & team player. Strong interest in research work. Highly motivated, independent and resourceful. We
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deep learning. You will contribute to several high-impact projects addressing hydrological extremes and their feedbacks within climate and human systems. Your work will have real-world impact, providing
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data with good utility. This project involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation
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at the Institute and affiliated academic departments. What you’ll do: Designing, developing, and deploying modern AI/ML models—including deep learning, foundation models, multimodal architectures, and generative
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.) during medical imaging and radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these