Sort by
Refine Your Search
-
Employer
- Texas A&M University
- Stanford University
- Oak Ridge National Laboratory
- Bowdoin College
- Carnegie Mellon University
- Cornell University
- Harvard University
- Princeton University
- University of North Carolina at Chapel Hill
- Yale University
- University of Washington
- Duke University
- Pennsylvania State University
- Argonne
- Brookhaven National Laboratory
- Northeastern University
- University of Miami
- University of Minnesota
- Campbell University
- Loyola University
- Massachusetts Institute of Technology
- New York University
- Purdue University
- Research Center for Molecular Medicine (CeMM), ÖAW
- Rutgers University
- Saint Louis University
- Texas A&M AgriLife
- The University of Arizona
- Tufts University
- University of Arkansas
- University of California Irvine
- University of Connecticut
- University of Florida
- University of Illinois at Urbana-Champaign
- University of Kentucky
- University of Nebraska Medical Center
- University of Texas at Arlington
- Vanderbilt University
- 28 more »
- « less
-
Field
-
deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
-
skills, especially in quantitative and/or mixed methods. Experience with statistical analysis including cluster analysis Comprehensive computer skills, with the ability to learn and utilize new and
-
. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and
-
experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
-
computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
-
, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in
-
, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
-
analysis, GIS, and large environmental datasets Experience developing predictive or machine learning models for environmental systems Demonstrated record of peer-reviewed publications Experience
-
Integrate multi-omics data with clinical, cognitive, and imaging phenotypes in longitudinal cohorts Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival analysis
-
applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA