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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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or experience in nontraditional research publication methods and collaborative notetaking software (e.g., Roam Research, Obsidian, Notion). ? Familiarity with cloud computing and machine learning techniques
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advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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Scholar appointments to a total of five years, including postdoctoral experience(s) at other institutions. The University of Washington and the International Union, Automobile, Aerospace and Agricultural