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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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machine learning with single-cell genomics, spatial omics, and systems biology, supported by strong collaborations across UBC and internationally. Project Recent advances in single-cell and spatial omics
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast
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as an integrated analytical framework, we apply comparative risk assessment, disease modeling, machine learning, and survival extrapolation methods to systematically quantify the long-term impacts
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digital health Computing systems and networks Cybersecurity Human-computer interaction Machine learning and artificial intelligence Software engineering Position details Positions are intended
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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in Machine Learning for Cognitive