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The Postdoctoral Scientist at the Urbanowicz Lab in the Department of Computational Biomedicine develops and applies machine learning and AI methodologies to complex biomedical data. Responsibilities include designing experiments, implementing algorithms, analyzing large-scale data, and collaborating on research projects targeting diseases such as sleep apnea and cancer. This role supports breakthrough biomedical research by creating interpretable, scalable, and trustworthy computational tools that enhance healthcare outcomes.
Principal Investigator, Dr. Ryan Urbanowicz , is seeking a Postdoctoral Scientist to join the Department of Computational Biomedicine!
The primary focus of the URBS Lab (Unbounded Research in Biomedical Systems) is the development, evaluation and application of machine-learning (ML) and artificial intelligence (AI) tools/methods targeting various biomedical data types and problems. We develop and apply methods that (1) automate and provide rigor to machine-learning analyses, (2) can detect complex patterns of association, (e.g., epistasis and heterogeneity), (3) are interpretable/explainable to promote trust and translational adoption, as well as to identify and address sources of bias, (4) scale to “big data” and (5) flexibly adapt to common data challenges, (i.e., missing values, class imbalance, consideration of covariates). We aim to make ML and AI tools that are applicable to a wide variety of biomedical problems and that are accessible, reliable, reproducible, flexible, user-friendly, computationally efficient and transparent. Our lab holds particular interest in the research of rule-based ML, automated ML, feature selection and evolutionary algorithms. To learn more, please visit:Cedars-Sinai | Urbanowicz Lab
Are you ready to be a part of breakthrough research?
The URBS-lab focuses on the development and application of machine learning, artificial intelligence, and statistical methodologies targeting a variety of domains and data types within biomedical research. Emphasis is placed on developing methods that are interpretable/explainable, scalable to ‘big data’, and capable of detecting complex patterns of association. Current research focuses on automated machine learning, rule-based modeling, feature selection, and evolutionary optimization. Other areas of interest include (but are not limited to) rare-variant analysis, data simulation, data integration, heterogeneous patient subgroup identification, time-series analysis, deep learning, and identifying and correcting for biases. We are a highly collaborative lab with access to EHR, genomic, and other ‘omics’ data in areas of research such as obstructive sleep apnea, congenital heart disease, pancreatic cancer, transplantation donor-recipient matching, and hospital readmission.
Working independently but in close cooperation and in consultation with Dr. Ryan Urbanowicz and other research scientists, the Postdoctoral Scientist will perform routine and complex computational, mathematical, and statistical procedures throughout the training period. As a Postdoctoral Scientist you may develop, adapt, and implement new techniques, algorithms, analysis pipelines, and software, as well as analyze, interpret, summarize, and compile data.
Primary Duties and Responsibilities:
Education:
Experience and Skills:
machine learning, artificial intelligence, computational biomedicine, bioinformatics, data analysis, algorithm development, big data, biomedical research, statistical modeling, feature selection