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Field
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Are you passionate about deep learning, generative AI, agents? We are seeking a postdoctoral researcher to join the research group led by Ivan Titov ( http://ivan-titov.org/ ), part of the natural
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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appointment. See the following table(s) for the current salary scale(s) for this position: https://www.ucop.edu/academic-personnel-programs/_files/2024-25/oct-2024-scales/t23.pdf . The current minimum salary
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of the appointment. Demonstrated experience with deep learning methods or sophisticated mathematical frameworks applied to large-scale or scientific datasets. Experience working with observational seismology data (e.g
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to the academic environment through teaching and supervision activities. If you enjoy combining deep scientific questions with computational innovation—and want your work to support the future of sustainable
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for the analysis of measurement data -To determine correlation parameters for different samples and implementation of deep learning for signal analysis -To perform experimental set-up for measurements of mice with
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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 LLMs
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well as the underlying signaling network for a variety of diseases including aging, cardiovascular diseases, brain diseases, cancer, et al., through novel statistical or deep learning methods development and application
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Sciences at the University of Amsterdam (UvA). BDA works on the development of methodology for data mining, machine learning/deep learning, data fusion, and modelling and application of these methods
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large