30 machine-learning "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Geesthacht" Fellowship positions at City of Hope
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, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic data and health record information, to develop
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, shaping the future of medicine through cutting-edge research. A Postdoctoral Fellowship position is available in the RSP Lab led by Dr. Sklavenitis Pistofidis. The RSP Lab (https://rsplab.org ) leverages
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models to study metabolic diseases is preferred. For a complete list of Publications please visit here: https://www.ncbi.nlm.nih.gov/myncbi/1J54E41I5YVku/bibliography/public/ Your qualifications should
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. Gu’s lab and research, please visit: https://gu-lab.net and https://www.cityofhope.org/Zhaohui-Gu As a successful candidate, you will: Design and execute experiments to understand the molecular
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Fellowship position is available in the RSP Lab led by Dr. Sklavenitis Pistofidis. The RSP Lab (https://rsplab.org ) leverages advanced multi-omic technologies, coupled with experimental perturbations and
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for publication. Preferred qualifications: Experience with super-resolution microscopy, coding (Phyton, Matlab), and machine learning is preferred. City of Hope employees pay is based on the following criteria
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Oncology Department at City of Hope is the largest Radiation Oncology enterprise in the southern California with more than 25 Linear Accelerators (Linacs) including special designed machines such as MR
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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involves developing and application of interpretable machine learning methods combined with physics based molecular dynamics simulations to uncover allosteric regulation of disordered regions in the dynamics
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. This project involves the development and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in the dynamics of G-protein coupled receptors to enable