Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
-
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD offer (starting date: Fall 2026) Context and Objectives: Near
-
impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
-
holder. The specific objectives of the post holder will be subject to review as part of the individual professional assessment process. SKILLS have good programming skills in R. have a good level of
-
environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
-
reactions using an additive based screening approach. 3) Development of a multi-objective Bayesian optimization algorithm for the reoptimization of reactions. 4) Application of the reoptimized key reaction in
-
on population change objectives. It will also provide a better understanding of what data needs to be collected (and the related sampling plans) to address these issues. To meet these objectives, the project will
-
experience. This PhD project investigates how such radio signal cues can be identified, selected, and integrated into learning-based navigation frameworks. Research Objectives: The PhD candidate will
-
isotopes, TIMS and ICP-MS. B3 Knowledge and experience of project-specific technical models (e.g., Bayesian modelling), equipment or techniques including high-precision CA-ID-TIMS and LA-ICPMS U-Pb
-
statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will quantify and analyse uncertainties in the design and operational performance