31 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Université de Bordeaux " research jobs at City of Hope
Sort by
Refine Your Search
-
Listed
-
Field
-
, 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
-
, 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
-
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
-
. 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
-
-life environmental exposures and the prenatal origins of childhood leukemia, for example in our ReCord Study (https://record.umn.edu ). Applicants are required to submit a CV accompanied by a cover
-
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
-
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
-
successful candidate, you will: This project with Dr. Nagarajan Vaidehi involves developing and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in
-
experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
-
. 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