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of excellence and a culture marked by ambition and a deep, practical engagement with challenges facing society. We continue to produce talented alumni and draw faculty and staff eager to be a part of the
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, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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to solve problems independently • Good written and spoken English • Advantage: knowledge of AI methods, signal processing, and/or statistics; familiarity with basic methods and frameworks for deep learning
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scienceEducation LevelMaster Degree or equivalent Skills/Qualifications 1. Planning and conducting computational experiments on deep learning models for small molecules; 2. Implementing novel training techniques and
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equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and
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application of advanced deep learning models, with an emphasis on techniques such as knowledge distillation. The candidate will engage in research involving time-series analysis, including modeling, forecasting