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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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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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and field activities; (d) design and facilitate co-creation workshops with residents, district councillors and property owners to develop community spatial strategies; (e) assist in qualitative
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ink painting experience”. He/She will be required to: (a) assist in managing various projects on the development of Mixed Reality (MR) content in Unity to be displayed and interacted through MR
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project - “Uncovering the origin and development of space stems in negative electrical discharges through laboratory observations”. Qualifications Applicants should have a doctoral degree or an equivalent
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Department of Computing Research Assistant (Ref. 251218004) [Appointment period: around three months] Duties The appointee will assist the project leader in the project - “Developing an LLM-based
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The ability to work well independently and collaboratively as a team The capacity to develop and pursue a coherent research agenda The ability to develop a body of high-quality publications in scholarly
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in the research project - “Development of efficient and cost-effective equipment for angstrom-level precision polishing and its application in processing optical complex surfaces”. Qualifications
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Post-doctoral Fellow in the Department of Orthopaedics and Traumatology, School of Clinical Medicine
observation and molecular therapy. The successful candidate will leverage the unique clinical resources of the Greater Bay Area and the advanced research facilities at the University to develop innovative
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project - “Establishment of joint research centre on brain-inspired computing”. He/She will be required to: (a) conduct the research on the development of neuromorphic foundation models and brain