Sort by
Refine Your Search
-
that are both fast and adaptive? This thesis aims to develop a robust hybrid learning framework that lies at the nexus of online and offline learning. The developed algorithms should be able to benefit from
-
probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
-
, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies
-
strategies (e.g., feature attribution, counterfactual explanations, dialogue-based explanations, hybrid symbolic–ML approaches); develop user-facing explanation interfaces that connect algorithmic reasoning
-
telecommunications networks and urban infrastructures Change: Developing data analysis and modelling methods to understand the interdependency Impact: Better design to enhance telecom and urban performance Job
-
into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
-
for expensive new dispatchable generation capacity, enable deeper renewable energy penetration, mitigate grid congestion, and reducing CO₂ emissions at national scale. In this PhD project, you will develop
-
transmitters, DPD algorithms, and analog/mixed-signal challenges. For more details about the DISRUPT project, please visit: https://elca.tudelft.nl/Research/project.php?id=243 https://www.linkedin.com/posts/tu
-
(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related
-
packages to estimate variance components and/or in R; a desire to further develop advanced computational, modelling and algorithmic research skills, and utilize these developments into practical breeding