228 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Bournemouth University" positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics
-
Computation (MiC) Section at The Oak Ridge National Laboratory (ORNL) invites outstanding candidates to apply for a staff position in the Data Analysis and Machine Learning Group. This group focuses
-
Requisition Id 15990 Overview: We are seeking an Instrument technician who will focus on audio-visual (AV) support and personal computer (PC) maintenance. This position resides in
-
Requisition Id 15926 Overview: We are seeking a Research Data Steward to join the Data Science and Engineering for Nonproliferation group. The selected candidate will focus on the stewardship
-
for health research projects. The research activities include HIPAA compliant research data that has been entrusted to ORNL. As such, you will have the opportunity to work on some of the most challenging and
-
Requisition Id 15997 Overview: We are seeking a postdoctoral researcher who will focus on atomistic simulation and data science approaches. This position resides in the Chemical Transformations
-
Requisition Id 15974 Overview: We are seeking a Computer Engineer with a focus on Hardware Vulnerability Research who will support the Cyber Resilience and Intelligence Division in the National
-
attracting scientists from all over the world conducting world-class research in physical, chemical, and materials sciences. More details about the instruments and their scientific impact can be found at https
-
to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
-
postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI to enable scalable, reproducible AI workflows on leadership-class systems