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development of explainable and interpretable classical and quantum deep learning algorithms - Design and development of hackathons and other collaborative methodologies to promote the control of artificial
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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(e.g., deep neural networks, surrogate modeling, reinforcement learning, scientific AI). Willingness to work on interdisciplinary problems at the intersection of AI, control, and physical sciences
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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various concepts in welding and related mechanical engineering processes and cleaning existing datasets. Creating deep learning models. Using deep learning to create a robust model that predicts welding
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | 14 days ago
are available to obtain a deep understanding of the animal and plant communities in which bonobos are embedded. The candidate will be responsible for incorporating long-term data available on various analog and
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, or a related STEM field. Experience with EHR data, machine learning or deep learning, natural language processing, medical imaging, or large language models (LLMs) is highly desirable. Familiarity with
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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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Position Details Position Information Job Title Postdoctoral Scholar Position Number THCAS-DEEP-PDS-FALL26 Vacancy Open to All Candidates Department AAH Earth Environment and Planning Department