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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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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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to their favorable cost profile and anticipated tolerability; however, the development of innovative interventional clinical studies requires a detailed and comprehensive understanding of their underlying biological
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to develop a new class of therapeutics based on bottom-up Synthetic Immunology. Inflammatory signals lead to epigenetic remodeling that can impact subsequent immune responses. This innate immune memory is
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input for project development are preferred. Applicants should submit a motivation letter, two reference letters or contact details of the referees, CV, and publication list. Job-ID: V000015706 Field
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research within the project. Tasks within the project: Cooperation in creating the project dataset of published Coptic letters, and attendance of the regular team meetings and seminars Development