Nikhef employs about 120 physicists, over half of whom are PhD students or postdocs, and about 100 staff to provide technical and other support. 

Nikhef does research into the smallest building blocks of matter and the play of forces among them. Scientists and technicians study these tiny particles both in collisions in large international accelerator laboratories, such as CERN in Geneva and in Germany and the United States, and in interactions between high-energy cosmic particles in the atmosphere or sea water. This relatively new combination of astronomy and particle physics has proved very fruitful and promises lots of exciting results yet to come. 

Nikhef develops and helps to build the complex detectors capable of registering these minuscule particles and works together with industry in the field of detector R&D. School pupils and teachers enjoy the challenge of the mysteries of astronomic particle physics and can frequently be found at Nikhef taking guided tours or participating in HiSPARC. 

Another important factor is the development of the Grid, the computer network required to store and process the measurement data. Ten years ago our researchers knew that the LHC experiments would produce several petabytes of data (1 petabyte = 1 million gigabytes), but in those days handling such large quantities of data was ‘impossible’.

LHC Computing Grid

The solution is the Grid, a collection of thousands of computers all over the world linked to one another by fast networks and equipped with software that enables them to operate as a single system on which scientists can store, distribute, edit and analyse their data. The system is not only used for subatomic physics: Nikhef’s computers are also used to compute data on brain research (e.g. using MRI scanners), biodiversity, simulations to find new drugs, and astrophysics.

Researchers at Nikhef have been developing the Grid since 2000. In particular they are working to develop better and better Grid software and pass on their knowledge of the Grid, so that all scientists with an ‘impossible’ data problem can take advantage of