Postdoctoral Researcher in Computational Modeling and Spatial Tumor Vasculature Analysis

Postdoctoral Researcher in Computational Modeling and Spatial Tumor Vasculature Analysis - Humanitas University

Cancer research today requires more than biological insight – it demands mathematical innovation, engineering precision, and advanced computational approaches. Soft tissue sarcomas remain among the most difficult tumors to treat due to the complexity of their tumor microenvironment and vascular architecture.
We believe that by quantitatively and spatially modeling this complexity, we can unlock new therapeutic strategies and improve patient outcomes.
Our mission is to transform cancer care through computational modeling and spatial analysis of tumor vasculature.
Position Overview
The VISTE study is pioneering a unique interdisciplinary approach at the intersection of engineering, mathematics, and oncology. Our research integrates:
Advanced mathematical modeling (Gaussian Processes, Bayesian inference, fractal geometry) to predict drug distribution in tumors
High-resolution image analysis to reconstruct 3D vascular networks from histological samples
Integration of spatial omics and computational pipelines to connect biological data with predictive quantitative models
We are recruiting a highly motivated Postdoctoral Researcher with strong expertise in mathematics, computational modeling, and image analysis engineering to join this innovative effort in computational oncology.
This position offers the opportunity to work at the frontier of cancer modeling, where engineering meets medicine and quantitative science drives clinical impact.
Key Tasks and Responsibilities
The successful candidate will contribute to the development of novel computational frameworks for understanding tumor vasculature and therapy response. Key responsibilities include:
Developing algorithms for image segmentation, feature extraction, and 3D vascular reconstruction
Building predictive models using Gaussian Processes, spatial regression, and Bayesian inference
Designing and deploying computational pipelines for multimodal data integration, including spatial omics and imaging
Collaborating within an interdisciplinary team to translate computational advances into clinically relevant insights
You are the perfect candidate if you:
Hold a PhD in Applied Mathematics, Biomedical Engineering, Computer Engineering, Computer Vision, Image Analysis, Bioinformatics, or a related discipline
Have strong programming skills in Python and/or R, with experience in high-performance computing (HPC)
Are familiar with spatial data analysis, statistical modeling, and quantitative approaches
Are motivated, independent, and able to work effectively in a collaborative research environment
Have an interest in cancer biology and a willingness to learn and engage with translational research questions
Additional requirements we value:
Experience with computer vision libraries such as OpenCV or scikit-image
Knowledge of vascular network modeling, fractal geometry, or advanced statistical learning
Previous work integrating imaging and omics data
What we offer
You will join a stimulating and interdisciplinary research environment at the forefront of computational cancer research. The position provides:
The opportunity to contribute to cutting-edge, high-impact oncology projects
Access to state-of-the-art computational and imaging infrastructure
Collaboration with experts across mathematics, engineering, and cancer biology
Professional growth within an innovative and internationally connected research network

All candidate data collected from the application shall be processed in accordance with applicable law: Dlgs 198/2006 e dei Dlgs 215/2003 e 216/2003;  privacy ex artt. 13 e 14 del Reg. UE 2016/679.