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research project in any of several areas relevant to this topic, and to explore multiple lines of research in parallel. Most projects employ a combination of bacterial genetics, high throughput genetic
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to this topic, and to explore multiple lines of research in parallel. Most projects employ a combination of bacterial genetics, high throughput genetic screens, biochemistry, ribosome profiling (ribo-seq
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 2 months ago
projects including genomics and bioinformatics, oceanographic and coastal modeling, computational models of chemical structures, video and photo processing, and sensor development. Many of these labs
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computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management and analysis solutions that aid in the storage, investigation and
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applications with related expertise. Experience with sequencing approaches to study RNA turnover or with massively parallel reporter assays(MPRAs) will be beneficial. Must Have Bioinformatics experience in
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application domains: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum‑classical algorithms to tackle domain‑specific, computationally
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computing and quantum advantage is a prospective topic across four application domains: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum
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: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum‑classical algorithms to tackle domain‑specific, computationally demanding problems. Methods
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of the six schools of Aalto University. Our portfolio covers fields from natural sciences to engineering and information sciences. In parallel with basic research, we develop ideas and technologies further
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laboratory Our research focuses on large-scale pan-cancer genomics to gain insight into the genes, mutational processes and evolution of cancer. Our work is highly data-driven, with a focus on large-scale data