29 data-scientist Postdoctoral positions at Oak Ridge National Laboratory in Postdoctoral
-
to both scientists and non-scientists in both written and oral presentations. Self-disciplined work ethic and eagerness to tackle challenging research problems Strong desire to work in a team environment
-
methodologies as well as subsized mechanical testing methodologies on highly irradiated materials (either using ions or research reactor irradiation data) for establishing performance envelopes for materials
-
alongside data scientists, software engineers, statisticians, and domain experts, publish and release your work openly, and apply these methods to nuclear nonproliferation and nuclear fuel cycle problems
-
for collaborative use within the EGRET project. Work closely with remote-sensing scientists, hydrologists, environmental scientists, process-based modelers, and computational scientists across DOE laboratories and
-
team of ecologists, biogeochemists, modelers and data scientists across ORNL and the broader DOE E3SM and Environmental System Science communities. Present research findings at scientific conferences and
-
team of ecologists, biogeochemists, modelers and data scientists across ORNL and the broader DOE E3SM and Environmental System Science communities. Present research findings at scientific conferences and
-
data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana. This candidate will directly
-
(ORNL) but will involve close (daily) collaborations with scientists/team members across the laboratory, specifically the Energy Storage and Conversion Group. Potential candidates are expected
-
for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
-
of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as