16 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "UNIV" "UNIV" research jobs at Hong Kong Polytechnic University
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Neural Networks (SNNs); (c) have strong proficiency in PyTorch programming and model development; and (d) possess a basic understanding of brain-inspired learning methods and Large Language Models
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mHealth-based healthcare professional-lay health worker partnership program (mHLPP) among community-dwelling impoverished older adults”. He/She will be required to: (a) coordinate the day-to-day
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application; (b) experience in conducting human neuroscience research and/or be proficient in computer programming, e.g. Matlab and Python; (c) a good command of both written and spoken English; and (d
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posts should: (a) also have expertise in operations research, operations management, power grid and mobility; and (b) have expertise in data analysis, computer programming and using commercial
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, the Head of Unit or their delegates. Qualifications Applicants should have: (a) a doctoral degree in Programming or an equivalent qualification and must have no more than five years of post-qualification
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in unity game engine and programming with hands-on experience in developing interactive 2D/3D applications or games; (d) have knowledge of 3D modeling, animation or interactive design tools, such as
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performance simulation software (e.g. EnergyPlus, CFD, COMSOL) as well as in programming languages like Python and familiarity with AI/machine learning frameworks (e.g. PyTorch, TensorFlow). Applicants
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background in 3D perception, including segmentation, localization, mapping and multi-modal sensor fusion; (b) proficiency in programming languages such as Python and C++; (c) demonstrated ability
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) have a good knowledge of computer vision and programming skills; and (c) be willing to learn research methods, data processing and data analysis. Applicants are invited to contact Prof. Lawrence W. C
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and energy materials. Preference will be given to those with knowledge of computer programming, AI and/or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766