Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, Geosciences Posting Number req27000 Department Geosciences Department Website Link https://geo.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights The Department of Geosciences
-
BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
-
compensate for the uncertainties of vRES, reducing financial penalties while enabling more reliable provision of ancillary services (AS) to the grid. For cascaded run-of-the-river HPPs, the storage capacity is
-
reactors, with particular emphasis on statistical methods, uncertainty quantification and machine learning to improve the accuracy and computational efficiency of fuel-performance analyses. The work
-
intellectually stimulating environment to enable high quality work in strategic directions that are of significant impact to industry, science and technology. For more details, please view https://www.ntu.edu.sg
-
Future robots must perceive, predict, communicate, and decide reliably in complex, human–robot shared environments. The Mobile Robotics (https://www.aalto.fi/en/department-of-electrical-engineering
-
predictions with available furnace inspection or rebuild data. Evaluate refractory wear rate, remaining thickness, and remaining service life trends. Perform data analysis, visualization, uncertainty evaluation
-
is £20,780, RTSG £8,400, full Tuition Fee covered). Hours: Full time Contract: Contract/temporary Closing date: 31/01/2027 The project: Decision-making under uncertainty is a fundamental challenge in
-
Biophysical and Biomedical Measurement Group - NIST | Gaithersburg, Maryland | United States | 13 days ago
biological signals to quantitative mechanism and uncertainty. What you will do: - Design and execute research on measuring ionizing-radiation dose using polymer chain scission reactions. - Prepare and
-
. To this end, domain decomposition techniques, uncertainty quantification, and reduced models—possibly based on neural network training—will be considered, along with the development of theoretical results