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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected
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one of the following domains are highly desirable: deep learning models on natural language processing or computer vision, advanced analysis of fMRI using encoding or decoding models, computational
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under the guidance of Prof. Ivan Nourdin. Your role Conduct research in machine learning, deep learning, and probabilistic modeling, with a focus on real-world applications Disseminate research findings
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learning in imaging analysis, clinical trials, epidemiology, genetic study, big data analysis, R language or related statistic software, or/and Python software, or programming skills of deep learning tools
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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to join our team at the CRSA to develop AI models, specifically deep learning approaches, to analyze and predict key climate variables such as precipitation and temperature which are essential
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at a reduced cost. In this context, we are looking for a highly motivated postdoctoral researcher to join our team at the CRSA to develop AI models, specifically deep learning approaches, to analyze and
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Qualifications: Experience with aging populations or neurodegenerative diseases Familiarity with deep learning and advanced statistical approaches to neuroimaging data Prior publications in relevant areas Required
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Professor Hing Leung and the wider multi-disciplinary research team. We are looking for candidates with experience/strong interests in learning some of the following: deep learning, medical imaging, and
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, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing ● Emerging AI/ML models and hardware