Postdoctoral fellow in the analysis of Spatial Omics data

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 nextgeneration molecular pathology. Your research will focus on answering critical questions related to both healthy and diseased liver utilizing data from improved wetlab protocols from multiple biological models complemented by inhouse generated spatial omics data with optimized RNA & Protein marker panels (MERFish Vizgen MACSima Miltenyi). More details on the project can be found below.

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 AIdriven computational pipelines.

We will aim to develop novel AIdriven 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 wetlab protocols that allow the codetection of RNA & Protein markers in freshfrozen PFAfixed or FFPEfixed preclinical mouse and clinical human liver samples. This will be complemented with tailored spatial multiomic 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 wetlab protocols and matching optimized RNA & Protein marker panels with the development of novel AIdriven 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 nextgeneration 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 Bioengineering or equivalent.
• Proven track record of successful research and highranked 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
• Highperformance 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

We offer

• Competitive stipend and benefits package
• Fully funded Postdoctoral fellowship for up to 4 years but encouraged to apply for a national Postdoctoral fellowship (e.g. FWO)
• A versatile and challenging academic position with very diverse contacts in a worldclass research environment operating at an international level and various opportunities to broaden your expertise.
• Collaborative and supportive research environment with expertise in both the generation (wetlab) and the analysis (drylab) of singlecell and spatial multiomic data.
• Access to stateoftheart compute & GPU infrastructure
• Attendance of national and international conferences
• Training courses in academic technical and career skills.

How to apply

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 ().