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to the supervision of PhD and/or Master’s students. Foster and sustain meaningful collaborations across local and national networks, including academic disciplines, professional sectors, relevant industries, and the
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curiosity and a deep sense of responsibility, they provide invaluable contributions to research and teaching, thus enriching our society. Are you also inspired and driven by the desire to make a meaningful
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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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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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, Computer Vision Models, Deep Neural Networks, and/or other 'AI' or Machine learning fields. We particularly welcome candidates whose work centers on the human or social aspects of the implementation
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to research. We are looking for a world-class scientist with expertise in the foundations of machine learning and its applications to deep learning and the ability and desire to: develop and lead research and
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quantitative, research-oriented postdoctoral scientist with strong AI/data science and statistical modeling expertise. The ideal candidate will have advanced coding skills and a deep interest in translating
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: Experience with deep learning frameworks like PyTorch Experience in LLM development and/or evaluation Language requirement: Good oral and written communication skills in English English requirements
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), e. a cumulative IF above 30, 2) experience and skills in the processing of medical signals and images, multimodal data, implementation of deep learning methods, data science, 3) programming skills and
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep