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IceLab invites you to join an information meeting on the IceLab multidisciplinary postdoctoral project call on April 29th, in KBC Glasburen and through the Glasburen Zoom.
During this meeting the project selection process will be explained, and researchers interested in finding a partner to submit a project proposal with will be offered the opportunity to pitch their idea. Register early to secure a spot to pitch a project.
The deadline to submit project proposals is May 27th.

Information Session Program

  • Introduction to IceLab and model of collaboration.
  • Information on the IceLab multidisciplinary postdoctoral program.
  • Pitch Session
  • Questions

Please register your intention to attend, or pitch at, this event. We will be in touch with those that choose to pitch with some specific information on how to put an open pitch together for this session.


Mattias Forsell, Professor, Department of Clinical Microbiology: presenting an open problem or idea that could benefit from a collaboration.

Abstract: We will be generating large amounts of immune receptor repertoire data from B cells (nucleotide sequences) and antibodies (proteomic data), by the use of NGS and Mass Spectrometry. The aim is to merge these datasets, and to put in context with germ-line repertoires of B cell receptors. The goal is to bridge gaps of knowledge at the nexus of adaptive B cell responses, namely the differentiation of activated B cells to memory cells or to antibody-secreting plasma cells.

To pursue this project, we are looking for a computational scientist to work with us with these complex data sets that can reveal new fundamental mechanisms for induction of immune responses.


Priyantha WijayatungaAssociate professor, Umeå School of Business, Economics and Statistics: presenting a research tool or approach that a collaborator might have data or an idea to apply it to

Abstract: General Measures of Statistical Dependence. In fields like genetics, so-called weighted gene co-expression network analysis (WGCNA) is used to detect highly associated genes. Though, to identify such associations Pearson’s  linear correlation works sometimes, there is a need to use some non-linear measures to model pair-wise functional-related dependencies among genes. Measures for such dependencies are so-called maximal information coefficient (MIC), mutual information (MI), etc.  However, there are some issues with them. Here I present a generalised dependence measure which needs appropriate discretization of the two statistical variable, as in the case of MIC. However, my normalization is better compared to MIC and more general. My measure can be approximated to continuous variable case too. I show that measuring dependence between two variables is just computational.

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