Postdoctoral Fellow in the Analysis of Spatial Omics Data
Postdoctoral Fellow in the Analysis of Spatial Omics Data
VIB
Ghent, Belgium
Project Overview
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).
Project details
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.
What you will do
- Develop and implement advanced algorithms for the analysis of spatial omics data.
- Integrate and analyze data from various (spatial) omics layers to gain insights into liver biology as an initial case study.
- Contribute to the development of new computational methods and tools for spatial omics data analysis, establishing next-generation molecular pathology pipelines that will be broadly applicable across tissues and species.
Profile
Essential:
- PhD in Bioinformatics, Computational Biology, Statistics, Computer Science, Artificial Intelligence, Physics, Engineering, Bio-engineering, or equivalent.
- Proven track record of successful research and high-ranked publications in bioinformatics
- Experience with applying AI (deep learning, probabilistic modelling, generative AI) or machine learning in the field of systems biology
- Proficient in Python or R programming
- Strong communication skills in English
Desirable but not required:
- Preference will be given to candidates with experience with
- Processing spatial omics data
- High-performance computing and software containers
- Key personal characteristics
- Strong interpersonal skills
- Ready to be the key link between multiple researchers
- Ready to mentor PhD students working on the same project
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