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Memorial Sloan-Kettering Cancer Center | New York City, New York | United States | about 2 months ago
) and H&E whole-slide images, all linked to comprehensive clinical annotations. The successful candidate will have the opportunity to lead the development of novel statistical and computational methods
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molecular biology, advanced imaging approaches, transcriptomics, flow cytometry and 2D/3D human skin models. Where to apply E-mail [email protected] Requirements Research FieldBiological sciences
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architecture, urban planning, urban design, design engineering, and real estate—as well as advanced research programs that cut across these disciplines and a robust public program of lectures, exhibitions, and
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cutting-edge omics, imaging, single-cell and computational approaches. https://cemess.univie.ac.at/ Your academic profile: Doctoral degree/PhD Two years of international research experience during or after
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About the role: We are currently looking for a passionate and motivated Principal Computational Biologist to work collaboratively within the Computational Biology Team. The Computational Biology
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cellular mechanisms of pathogens, and the role of microbes in health and disease. The department values a broad range of approaches in microbial pathogenesis, including cell biology, imaging, structural
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administrative systems and processes for a new or growing program. Interest in or exposure to imaging sciences, computational research, artificial intelligence, or related scientific fields. Prior experience
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sitting at a computer workstation for extended periods of time and performing tasks with repetitive motions (such as typing). Also involves some risk of injury from moving, lifting or positioning patients
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electrical, biomedical, mechanical, computer, or aerospace engineering or mathematics or computer science. The successful candidate will be driven, creative and team-oriented with excellent writing and
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with a strong background in mathematics, computer science, or machine learning. The work has a strong focus on developing new objectives or new architectures for medical deep learning and on new ways