Number of Applicants
:000+
17/06/24
Ghent, Belgium
This postdoctoral position is part of a larger project aimed at developing novel computational methods for spatial biology, establishing a new field of next-generation molecular pathology. Your research will focus on answering critical questions related to both healthy and diseased liver, utilizing data from improved wet-lab protocols from multiple biological models, complemented by in-house generated spatial omics data with optimized RNA & Protein marker panels (MERFish Vizgen, MACSima Miltenyi,). More details on the project can be found below.
With the development of powerful instruments that can measure the spatial distributions of hundreds of RNA molecules (e.a. MERSCOPE instrument), proteins (e.a. MACSima instrument) or metabolites (e.a. MALDI instruments), the technological challenge has moved from the ability to acquire multiplexed spatial data to the capacity to efficiently integrate and analyze these datasets and this will require the development of novel AI-driven computational pipelines.
We will aim to develop novel AI-driven algorithms that can extract two key spatial features from tissue sections: (i) the fundamental morphological and pathological features of the tissue (tissue architecture) and (ii) the spatial organization of the cells representing the building blocks of the tissue of interest, including their precise identities and activation states (cellular organization). To achieve this goal the host labs are developing improved wet-lab protocols that allow the co-detection of RNA & Protein markers in fresh-frozen, PFA-fixed or FFPE-fixed preclinical mouse and clinical human liver samples. This will be complemented with tailored spatial multi-omic panels of RNA & Protein markers that enable identifying each of the cells within the tissue, their activation states, precise location and cellular neighbors. Altogether, the project will aim to combine improved wet-lab protocols and matching optimized RNA & Protein marker panels with the development of novel AI-driven computational pipelines that can efficiently process spatial datasets and extract meaningful biomedical insights automatically.
Desirable but not required
Preference will be given to candidates with experience with
Key personal characteristics
Please complete the online application procedure and include a detailed CV, two reference letters, and a motivation letter.
For further information and questions, please send an email to Yvan Saeys ([email protected] ).
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