4 fully funded PhD scholarships in Systems Medicine
12 May 2026
4 fully funded PhD scholarships in Systems Medicine
Human Technopole is offering up to 4 fully funded PhD scholarships through the SEMM PhD Program in Systems Medicine. These scholarships are open to talented and motivated graduates – both from Italy and abroad – interested in pursuing doctoral research in the following areas:
Computational Biology
Functional Genomics
Population and Medical Genomics
Why Apply?
Selected candidates will:
Conduct research in dynamic, international teams led by world-class scientists
Work in state-of-the-art laboratories and facilities
Be part of a stimulating, interdisciplinary scientific community
Receive full financial support for the duration of their PhD
Who Should Apply?
We welcome applications from individuals of all backgrounds who hold a Master’s degree in a relevant field and who demonstrate a strong interest in biomedical research and a commitment to academic excellence.
Human Technopole is committed to promoting equality, diversity, and inclusion in science. All qualified applicants will be considered, regardless of gender identity, nationality, or background.
Human Technopole welcomes Wolfram Pönisch as a new Research Group Leader in the Computational Biology Research Centre – Biophysical Modelling and Simulations Programme. A theoretical physicist by training, Wolfram studies the stochastic morphodynamics of living systems: how cells and tissues change shape, fluctuate and use these dynamics to influence biological processes.
Human Technopole has opened a new Call for Access to its National Facilities, offering researchers across Italy the opportunity to use advanced technologies, specialised expertise and scientific support across HT’s shared research infrastructure.
At Human Technopole, we’re looking for curious and motivated young researchers to join two fully funded PhD projects within the PhD Programme in Data Analytics and Decision Sciences at Politecnico di Milano.
The new Multimodal AI Across Scales Research Programme, within Human Technopole’s Computational Biology Research Centre, develops AI methods that connect biological data across modalities and scales, from molecules and cells through tissues and organs to patients and populations.
Researchers at Human Technopole have developed a novel machine learning-based method that transforms composite fluorescence microscopy images with overlapping signals into separate images revealing individual cellular structures. The tool is published in Nature Methods, with experimental models and training data openly available on GitHub.
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