Sort by
Refine Your Search
-
the Bartesaghi Lab at Duke University to work in the development of image analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET
-
will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
-
correct code problems. Apply statistical and machine learning techniques to identify trends and insights in historical data. Design reports using tools such as statistics, graphs, images, and lists to help
-
, methods will include video- and machine-learning supported behavioral studies in mice, mouse genetics, fluorescent imaging, electrophysiology, pharmacological studies of irritant and thermosensory receptors
-
, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
-
system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine
-
) and bioinformatics tools Familiarity with data science, machine learning, artificial intelligence, natural language processing and applications to electronic health records and big data and
-
medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional statistics (desirable but not required
-
-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated evidence of excellent programmin g, collaboration
-
novel statistical methods motivated by medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional