41 solid-mechanics "https:" Postdoctoral research jobs at THE UNIVERSITY OF HONG KONG
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Ref.: 534416 Work type: Full-time Department: Department of Earth and Planetary Sciences (25300) Categories: Senior Research Staff & Post-doctoral Fellow, Research Staff Applications are invited
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Psychology, Neuroscience, Cognitive Science or equivalent, with 3 years’ related work experience, or a Ph.D. degree in a relevant discipline; (ii) solid statistical and computing skills; (iii) effective
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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. They should have solid knowledge of chemistry and biology, and an excellent command of spoken and written English and Chinese. Experience in bioinformatic data analysis including microbiome, virome and
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availability and satisfactory performance) Applicants should possess a PhD degree in Mechanical Engineering, Electronic Engineering, Automation, or a related field, along with a minimum of two years of relevant
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experience in image processing algorithms, with a solid foundation in computer vision algorithms. Preference will be given to those familiar with the principles of various medical imaging technologies, and
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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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or equivalent, with 3 years’ related work experience, or a Ph.D. degree in a relevant discipline; (ii) solid statistical and computing skills; (iii) effective communication skills in both written and
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the supervision of Dr. Shuofeng Yuan. Please find more details for research projects under Dr. Yuan’s lead at https://hkumicro.hku.hk/university-staff/dr-yuan-shuofeng/ . Pre-application inquiry with Dr. Yuan
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, including the validation of both standard and custom implant tests through computational simulation and mechanical testing. Develop computational models to support the design and application of patient