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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in optimization for statistical learning to join the Image, Data, Signal (IDS) department
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Department of Computer Science and the Data Science Institute. Learn more about the lab and its research here: https://nunez-comp-mental-health-cancer-care.github.io/ . RESPONSIBILITIES Reporting to Dr. John
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Biology, Bioinformatics, Statistics, or a closely related discipline, and have an strong record of research productivity. The ideal candidate will have experience in deep learning, generative models
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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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, physical chemistry, or surface chemistry) or related field completed by the start date Strong postdoctoral training in polymer-based materials, surface characterization, or sensor technologies Demonstrated
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Sessional Lecturer - CTL5704H - Special Topics in Teaching - Urban Education Curriculum and Pedagogy
stakeholders (including school educators, administration, community partners, professional networks). Deep knowledge and experience in contexts, policies and practices of the three divisions (Primary/Junior K-Gr
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7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed