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for translatome analysis Expertise in integrating large-scale multiomic datasets, including machine learning based approaches Excellent written communication skills demonstrated by an outstanding publication track
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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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relationships and apply data-driven or machine learning approaches to guide molecular design and accelerate materials discovery Collaborate with internal and external stakeholders, including computational, data
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will have a strong research record in NLP, machine learning, or closely related areas, along with excellent communication and collaboration skills. We are especially interested in candidates with strong
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an exciting place to learn and
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Engineering, who have received recently a PhD degree or passed a viva. Expertise in finite element analysis (especially ANSYS), the skills allowing to create Python-ANSYS interfaces and using machine-learning
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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English
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. · Knowledge of molecular biology, cell biology, immunology, neuroscience, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning