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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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tuberculosis (TB) screening research, spanning the evaluation of novel, high-throughput molecular tests, innovative screening algorithms, and digital health tools. A core component of the role involves
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high-throughput sequencing to generate, analyze and integrate transcriptomic and epigenetic datasets To genetically (CRISPR/Cas9, shRNA) or pharmacologically (small molecules) perturb molecular targets
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bioavailability and protective effects of candidate DPs in mice, focusing on diabetic nephropathy and vasculopathy Your Profile A Master’s degree in biology, life sciences, or a related field High interest in human