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Research Assistant (Generative AI for Blockchain Designs)

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NATIONAL UNIVERSITY OF SINGAPORE logoNational University Of Singapore · Lower Kent Ridge Road, Singapore
S$48K–S$60K/yrFull-timeUnknownToday
BlockchainHugging FaceMachine LearningNLPPythonPyTorch
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About the role

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal. NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Generative-AI-for-Blockchain-Designs%29/31763-en_GB/ We regret that only shortlisted candidates will be notified. Job Description - In the era of rapidly advancing artificial intelligence, the project aims to explore how to leverage the power of large language models for meaningful blockchain consensus designs. The project will also look into applications in supply chain finance and is supported by DTC Singapore. The successful candidate will work with Asst. Prof. Qin Hanzhang and his team at NUS ISEM/AI Institute. Job Requirements - Holding a Bachelor's degree in Computer Science, Industrial Engineering, Mathematics, Finance, Financial Engineering or other related Engineering or Science disciplines. - Background in Natural Language Processing (NLP): Candidates should have some understanding of machine learning principles, particularly in the field of NLP. The candidate should be familiar with transformer architectures, such as BERT, GPT, or T5, and have experience in fine-tuning and training these models on large datasets. - Proficiency in Blockchain and Cryptocurrency: Prior exposure to understanding of blockchain mechanisms, especially consensus, is essential. The candidate should be proficient in writing smart contracts for specific purposes. - Programming Skills: Strong programming skills are preferred, particularly in languages commonly used in machine learning and smart contracts, such as Python and Solidity. Candidates should be adept at libraries and frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and Scikit-learn.


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