98 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University
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. Perform any other duties relevant to the research programme. Job Requirements: PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive
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technologies (lipidomics, proteomics), cell biology, molecular biology and bioinformatics to clarify the mechanisms of key molecular interactions, and support the development of early diagnostic and intervention
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background in Chemistry or Biology to work on nucleic acid structure and therapeutics. Key Responsibilities: Synthetic chemistry Cell Biology Bioinformatics analysis Engage and collaborate with multi
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these codes in C++ or Fortran Adopting these codes for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in
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organoid models for experimental use. Process and handle human biospecimens (e.g., nasal brushes, tissue biopsies) in compliance with institutional biosafety and ethical standards. Design and perform
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epidemiology, bioinformatics, computational biology). Candidates who have successfully defended their thesis or dissertation are welcome, subject to evaluation on a case-by-case basis. Experience analyzing large
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and to develop accurate cancer risk prediction models. This role involves developing computational pipelines, conducting statistical and bioinformatics analyses, and integrating multi-omics data
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imaging, and NMR data acquisition/analysis. Perform bioinformatic sequence analysis to guide experimental design and correlate protein sequence features with amyloid assembly properties. Collaborate with PI
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guidelines related to clinical and animal studies Proficiency in multi-omics data analysis, and bioinformatics, preferably preferred Excellent organizational skills, attention to detail, and the ability
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model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy