Sort by
Refine Your Search
-
). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts
-
uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
-
of transportation and integration of renewable energy. Despite falling costs, uncertainties about battery lifetime challenge sustainability. Accurate lifetime prediction is crucial for effective management, enhancing
-
union. The period of employment ends on 30-Jun-2027. Preferred start date: January 2026. You can read more about career paths at DTU here . Further information If you are in any way in doubt of whether
-
inaccuracy, irregular sampling grids, variations in measurement conditions, and other measurement uncertainties. The responsibilities of a research assistant involve developing technical solutions aligned with
-
. Responsibilities and qualifications As part of the DECIDE project you will be working on developing next generation of tools for decision making under uncertainty. You should have at least basic knowledge about
-
experience in scientific writing and publication in peer-reviewed scientific journals Research experience in some of the areas of process-based crop modeling, uncertainty characterization, digital agronomy
-
-based crop modeling, uncertainty characterization, digital agronomy, remote sensing Additional qualifications Further, we will prefer candidates with some of the following qualifications: Teaching and