Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Groningen
- European Space Agency
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University
- University of Twente
- University of Twente (UT)
- University of Twente (UT); Enschede
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- Wageningen University & Research
- Leiden University; Leiden
- Maastricht University (UM)
- Radboud University
- Utrecht University
- Wageningen University and Research Center
- CWI
- Erasmus University Rotterdam
- Maastricht University (UM); Maastricht
- The Netherlands Cancer Institute
- The Netherlands Cancer Institute; Amsterdam
- University of Groningen; Groningen
- Utrecht University; Utrecht
- Vrije Universiteit Amsterdam (VU)
- Wageningen University & Research; Wageningen
- 17 more »
- « less
-
Field
-
System applications, system/segment/payload system architectures and performance, signal-in-space and signal processing, GNSS receivers, algorithms, OD&TS, formation-flying and radio frequency/optical
-
equivalent) in Computer Science, Data Science, Artificial Intelligence, Computational Social Science, or a closely related discipline. The ideal candidate has a strong interest in algorithms, machine learning
-
programme in 2008. Through the CCI, ESA is developing a suite of global data records of key components of the climate system, known as essential climate variables (ECVs). The climate-quality datasets produced
-
space systems; Investigating and implementing PQC algorithms to safeguard space data systems against quantum computing threats; Evaluating the performance and security of PQC solutions in the context
-
-class expertise from eight Dutch Universities, five Research Institutes and relevant societal stakeholders that play a major role in research and management of the North Sea. The six-year program (2025
-
tumour response to treatment can then bring this much-needed clarity, and implementing this understanding into computational tools can provide the potential for rapid clinical response to patients
-
for recognition of planetary materials from multispectral datasets. Interns are sought to contribute to the ongoing development of Machine Learning algorithms for recognition of planetary materials from
-
Division, Systems Department, Directorate of Technology, Engineering and Quality. The Future Engineering Division, in support of the programme directorates, is responsible for developing and providing