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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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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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for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models to real-world data (RWD). Create innovative tools and solutions to extract deeper
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Data Scientist (Artificial Intelligence). We now invite applications for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models
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multimodality deep learning model development in different sarcomas. Publications in related fields will be a strong advantage. Opportunities for publication and independent development will be available
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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, transcriptomics data, Nanopore long read sequencing analysis and/or multimodality deep learning model development in different sarcomas. Publications in related fields will be a strong advantage. Opportunities for
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. Applicants should have a Ph.D. in Biological/Biomedical Sciences, Microbiology, Bioengineering, or a related field, with experience in cell culture, molecular and cell biology, and animal models. Skills in
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assessing the impact on disease progression. Develop relevant disease models to assess therapeutic efficacy of a specific treatment. Demonstrate understanding of the disease pathology for better design of
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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