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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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separation technologies from different points of view. The work requires deep scientific understanding of the related physical and chemical phenomena, exploration and development of novel and existing
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graduation. An excellent academic record, a deep understanding of immunology/immunotherapy (human/mouse) as well as practical experience of immune cell biology, cell culture, gene edition (crispr/cas), flow
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deep learning frameworks What We Offer: Innovative Projects: Work on high-impact health research and decades of extensive real-word health data using the latest analytic and AI methods in
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-of-the-art research on particle-related separation technologies from different points of view. The work requires deep scientific understanding of the related physical and chemical phenomena, exploration and
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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, normalization, dimensionality reduction) to downstream interpretation (differential expression, gene set enrichment, and cell type annotation). Implement Machine Learning Approaches, including deep learning
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transmon qubit at millikelvin temperatures . In addition, we have demonstrated millikelvin components for generating coherent microwave drive pulses and reset of qubits . Thus as a deep professional in cQED
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the Department of Computer Science, and the person is expected to engage in deep collaboration with research groups at Faculty of Science and Faculty of Pharmacy to enhance cutting edge research in the area. The