14 computational-biology-phd Postdoctoral positions at King Abdullah University of Science and Technology
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. A healthy work-life balance in a work-play-live environment. We are looking for an independent scientist, who is passionate about synthetic biology and generative biology, and is excited by
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Postdoctoral Researchers to work on research problems at the interface between a selection of the following areas: - Bayesian inference for deep learning - Generative modeling - Computational statistics
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in genomics and molecular biology to address biological questions in the area of disease resistance, genome biology, and evolution of wheat. Our mission is to perform impactful curiosity-driven
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Applicants must have a PhD in Computer Engineering, Computer Science, or Electrical and Computer Engineering, and have published their research in prestigious conferences and journals in related
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language processing, computational biology and healthcare. Our group focuses on important and fundamental open problems in machine learning research and challenging applications in diverse fields (e.g., biology and
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skills in bioinformatic data analysis, pipeline implementation (and possibly development), and programming. The ideal candidate will have a Ph.D. in bioinformatics, computer science, biotechnology, biology
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holding a PhD in chemical, environmental or process engineering, to apply for a full-time post-doctoral fellowship position in the field of water desalination, focusing on the development of an artificial
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/ ) at KAUST. Our group combines innovative approaches in genomics and molecular biology to understand wheat genome biology and evolution. Our mission is to contribute to curiosity-driven research of high impact
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King Abdullah University of Science and Technology (KAUST) in Saudi Arabia is seeking a creative and ambitious postdoctoral researcher to join Prof. Hylke Beck’s research group within the Climate and Livability Initiative (CLI). The researcher will leverage machine learning (ML) to enhance...
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling