Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft)
- University of Twente
- Utrecht University
- Leiden University
- University of Amsterdam (UvA)
- Wageningen University & Research
- ;
- DIFFER
- Maastricht University (UM)
- Radboud University
- University of Twente (UT)
- 2 more »
- « less
-
Field
-
, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
-
Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
-
sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in healthcare, mobility, public governance, and healthy living. Each PhD position is embedded in one work package and
-
storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
-
. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
-
is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within
-
machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
-
sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
-
group? Do you enjoy creating complex machines that have never existed before? Do you want to explore physics that nobody else has seen? Maybe you want to join our team as a PhD on our journey
-
from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover