13 big-data-and-machine-learning-phd Fellowship positions at Nanyang Technological University
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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computational materials science techniques (DFT, MD, machine learning force fields) with data-driven approaches. Design and implement high-throughput experimental workflows for thermal conductivity and phonon
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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. The role will focus on developing machine learning and mathematical optimization solutions for electric vehicle fleet charging optimization under different constraints. Key Responsibilities: Formulate
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large language models (LLMs), including
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian