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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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geophysical survey and machine learning algorithms. Mainly responsible for processing geophysical/geomechanical data and analysing the data using machine learning. Conducting research, supervising undergraduate
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-UAS technologies o Multi-agent systems and swarm robotics o Autonomous navigation and guidance o Computer vision and perception o Machine learning
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Job Summary: The Research Fellow will be responsible for undertaking in-depth research and innovation in machine learning, data science, and artificial intelligence on a novel urban intelligence
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• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.) • Literature review/summarizing skills
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the supervision of the Principal Investigator, including but not limited to the following: Develop new computational tools through the application of AI / deep learning / machine learning / statistics on spatial
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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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pediatric health services research and child population health research via didactics, structured mentorship, experiential learning through conducting rigorous and ethical research, and professional
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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational
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, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model-predictive control, etc