Human Technopole is looking for a curious, driven, and collaborative PhD candidate to join the Research group lead by Fernanda Pinheiro as part of the EU-funded ENDAMR (Exploring Novel Drivers of Antimicrobial Resistance), a Marie Skłodowska Curie Actions (MSCA) Doctoral Network. This PhD project will develop mechanistic models grounded on microbial physiology to understand how interactions among mutually dependent bacteria influence antibiotic tolerance, community-level perturbations and evolutionary trajectories toward antimicrobial resistance.
We’re seeking someone with a background in bioinformatics, data science, computer science, or applied mathematics who is excited about applying computational tools to advance biomedical research.
Location: Milan, Italy
Duration: 3 years, fully funded (Marie Skłodowska-Curie Actions)
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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