Research Fellow (Computer Science/AI/Computational Biology/Bioinformatics)
Nanyang Technological University- Posted 3 hours ago
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Job Description
Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world's top universities. NTU's College of Computing and Data Science (CCDS) is a leading college known for its excellent curriculum, outstanding research, and world-renowned faculty. Located in the heart of Asia, NTU CCDS is shaping the future of AI, Data Science, and Computing.
We are seeking a highly motivated and innovative Research Fellow (AI / Computational Biology) to join our cutting-edge initiative at the intersection of artificial intelligence and RNA therapeutics. This position is part of a collaborative effort across NTU CCDS and LKC School of Medicine, developing frontier AI methodologies to revolutionize RNA-based treatments.
Key Responsibilities:
Develop and deploy state-of-the-art machine learning/deep learning models for RNA sequence design, structure prediction, and property optimization.
Process, curate, and model high-throughput screening and genomics datasets.
Collaborate closely with wet-lab biologists and clinicians to validate AI predictions experimentally.
Stay current with advancements in AI4Science and prepare scientific publications for top AI conferences and high-impact journals.
Present research findings to multidisciplinary academic and industry partners.
Job Requirements:
PhD in Computer Science, AI, Computational Biology, Bioinformatics, or a related quantitative discipline.
Strong background in machine learning/deep learning with proficiency in PyTorch, JAX, or TensorFlow.
Experience with biological sequence analysis, generative models, or computational drug discovery is strongly preferred.
Excellent programming, analytical, and collaborative communication skills.
Eagerness to work in an agile, interdisciplinary research environment.
We regret that only shortlisted candidates will be notified.
More Info
Key Skills
computational drug discovery
generative models
biological sequence analysis
