14 model-checking Fellowship research jobs at Hong Kong Polytechnic University in Hong Kong
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learning models and are familiar with foundation models. Applicants are invited to contact Dr Ren Ge Gary at telephone number 3400 8595 or via email at [email protected] for further information
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in the research project - “Towards efficient stamp forming of thermoplastic composites through data-driven thermo-mechanical modelling”. Qualifications Applicants should have a doctoral degree or an
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foundation models, knowledge graph/ontology, federated learning or collaborative agents, AI security, etc.; (c) have experience in research proposal development; (d) have strong publication records in
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myopia therapeutics by next generation proteomics”. They will be required to perform: (a) biometric (refraction; A-scan) measurement on a guinea pig animal model; (b) drug application on animal eyes
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carbon air travel in Hong Kong”. They will be required to be involved real-time monitoring of the new biorefinery process for SAF production, signal/data analysis, AI-assisted modelling, sensor and data
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- “Ready to drive: A safety check”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no more than five years of post-qualification experience at the time
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outlining strategies for eventual clinical translation; and (c) project experience with brain disease models, focusing on mechanisms of neurodegeneration and the development of potential therapeutic
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- “Reliable industrial foundation models for trustworthy industrial robot fault diagnosis in automotive smart manufacturing”. He/She will be required to: (a) carry out research in advanced machine learning
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statistical software such as Mplus, Stata, or R; (h) be familiar with statistical methods including autoregressive models, structural equation modeling (SEM), dynamic SEM, psychological network analysis.; (i
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posts) [Appointment period: each for sixteen months] Duties The appointees will assist the project leader in the research project – “LocGPT: Understanding indoor localization by large AI models