Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- European Space Agency
- Leiden University
- University of Amsterdam (UvA)
- Wageningen University & Research
- Utrecht University
- Centrum Wiskunde en Informatica (CWI)
- Erasmus University Rotterdam
- SRON
- Tilburg University
- University of Groningen
- University of Twente (UT)
- AMOLF
- Amsterdam UMC, location VUmc
- Erasmus University Rotterdam (EUR)
- Radboud University
- Sanquin Blood Supply Foundation (Sanquin)
- The Open Universiteit (OU)
- Universiteit Leiden
- 10 more »
- « less
-
Field
-
, extensive computing resources and close day-to-day supervision within the group. Tasks and responsibilities: include developing reconstruction algorithms for the muon bundles recorded by KM3NeT; extracting
-
(Efstratios) Gavves, part of VISLab (Video & Image Sense Lab ) at the Informatics Institute (IvI), Faculty of Science, University of Amsterdam. CyPhai pursues a single north star: algorithms that understand
-
: include developing reconstruction algorithms for the muon bundles recorded by KM3NeT; extracting air-shower observables such as muon multiplicity, lateral separation and energy; using these observables
-
the project. What you will do Conduct original and high-quality research in machine learning and computer vision; Develop novel algorithms for adapting and specialising visual foundation models; Publish
-
with advanced photoreactors, modular LED light sources, inline analytics, and machine-learning algorithms for the autonomous optimization of photocatalytic reactions. Research will address key challenges
-
; Develop novel algorithms for adapting and specialising visual foundation models; Publish research findings at leading machine learning and computer vision venues such as CVPR, ICCV, ECCV, NeurIPS, and ICLR
-
estimates, and emission algorithms. The relevant processes at the individual vessel level can be modelled in detail, and their performance can be tracked in time and space. Corridor scale effects will be
-
algorithms. The relevant processes at the individual vessel level can be modelled in detail, and their performance can be tracked in time and space. Corridor scale effects will be simulated by aggregating
-
to both technical metrics and educational outcomes. Responsible AI and fairness auditing. Conduct algorithmic fairness validation of the CLARA system, develop documentation on data governance and GDPR
-
, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms, memory technologies, and neuromorphic hardware architectures for future