23 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at KU LEUVEN
Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
effects throughout the exposure process.The research is primarily quantitative and uses advanced analytical methods. Longitudinal data are already available, enabling you to start developing your first
-
objectives: • Develop a physics-based baseline time domain model for vibro-acoustic components using FEM/BEM and reduced-order modelling techniques. • Fuse sparse microphone or accelerometer data with model
-
with quantitative data sources. Key responsibilities include: Developing in-depth substantive and methodological expertise in labour market monitoring, prospective labour market research, and the
-
reshape and enhance the engineering design process by integrating technical and economic considerations within a coherent framework at both component and system level. Starting from historical data on
-
The candidate will develop and benchmark zero-shot multimodal fusion models for rare disease prediction, using melanoma as a use case. The project integrates spatial and single-cell multi-omics data
-
reason over their own data streams. Two strategies exist. Materialisation explicitly computes and stores every inferred fact; it supports powerful reasoning but is expensive and quickly overwhelms
-
predictable behaviour to avoid congestion or confusion. The research combines traffic modelling, data-driven analysis, and control design, and is closely connected to real-world pilots such as the
-
TURBO-IMPACT ( TURBOmachinery Innovative Manufacturing, Processing, Analysis, Characterization, and Topology; Website: https://cordis.europa.eu/project/id/101311350 ). The TURBO-IMPACT project, funded by
-
ethnographic methods, EMLIT aims to set a new theoretical and analytical standard for an inductive approach to world literature, to gather empirical data about the lived reality of literary emergence, , and
-
question is how such a twin can truly understand its environment, both on the data level (what is being observed and what it means) and on the processing level (how observations are collected, analysed