37 parallel-processing-bioinformatics positions at King Abdullah University of Science and Technology
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groundbreaking filtration and extraction processes. Key Responsibilities: Participate in and lead the development of lithium-ion battery recycling technologies. Develop advanced separation technologies including
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The Applied Mathematics and Computational Sciences (AMCS) program in the Computer, Electrical and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah
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and synthesis of microporous polymers and their applications in electrochemical processes for energy storage and conversion. You will independently lead a research direction within the group, while
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operation of the lab, mediating lab upgrade projects, and assisting in various research and administrative tasks. The Research Engineer will be a key support to Prof. Derya Baran and contribute
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Responsibilities: Lead pioneering research exploring biogeochemical processes in marine ecosystems, with a focus on some of the Red Sea's unique coastal, pelagic, deep and extreme environments Advance knowledge in
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to address fundamental marine science questions, employing bioinformatics and advanced computational techniques to describe interactions, connectivity and/or functional traits within and across marine
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level (Preferred) Experience with Red Sea oceanographic processes About KAUST KAUST is an international, graduate research university dedicated to advancing science and technology through
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI
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synthetic genomics (plant, yeast, mammal agnostic) Scalable expression systems and bioprocess engineering for alternative carbon source utilization, and process design for biosystem production Bioprocess
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict