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DTU Health Tech
Henrik Dams Allé B202,
DK-2800 Kongs Lyngby

Secretary Sébastien Sebbaha

E-mail address: dsfcm.dk@gmail.com

Archive for the Featured News Category

We are delighted to announce the 71st meeting of the Danish Society for Flow Cytometry (DSFCM), focusing on Cell Sorting.

The event will be held at:

  • Søauditorierne, Aarhus University on November 11, 2024.

Program Outline: (for more details see program)

  • 11 AM – 4 PM   Join us for an interesting program with the subject Cell Sorting
  • 5 – 7 PM            Join us for light dinner, a drink, and networking (members only)

About the event:

  • Time:         11-17 (19 o’clock for members)
  • Location:   Søauditorierne, Aarhus University
  • Address:    Bartholins Allé 3, 8000 Aarhus
  • Theme:      Cell Sorting

Registration:

Follow the link to register to the event ! https://events.au.dk/71stdsfcm

Registration deadline October 31st 2024 !

We are thrilled to announce that the next Nordic Flow Cytometry Meeting will be hosted in Denmark!

The prestigious University of Copenhagen, located at The Panum Institute, will be the venue for this anticipated event.

Save the Date:
Dates: 20-22 August 2025

Stay tuned for more details! We will be updating this page regularly with further information about registration, speakers, and the agenda. Make sure to bookmark this page and visit us again soon to get the latest updates on this exciting event in the world of flow cytometry.

Join us in Copenhagen for an enriching scientific experience at the Nordic Flow Cytometry Meeting 2025!

Important note :

The meeting will be offered as a PhD course by Copenhagen University. To enroll in the PhD course, you need to register both at the KU PhD school AND on the conference registration site.
Non-KU students will initially be put on the waiting list but will be accepted for the course after registration deadline.

Registration:

To the NFCM 2025 : www.nfcm2025.dk.

Additionally if you are attending it as part of your PhD course : High end flow cytometry and data analysis