Pasquale Ciarletta
3 weeks ago
Postdoctoral Position in Mathematical Oncology at Politecnico di Milano Politecnico di Milano in Italy
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral position; funding details, salary, and contract length are not specified in the post.
Country
Italy
University
Politecnico di Torino

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About this position
Postdoctoral Position in Mathematical Oncology at Politecnico di Milano, Milan, Italy.
The group of Pasquale Ciarletta is seeking one highly motivated postdoctoral researcher to work on mathematical and computational approaches to cancer research. The project focuses on mechanistic and data-informed models of tumour dynamics, with research at the interface of applied mathematics, computational science, and cancer biology.
Research themes include partial differential equations, multiscale modelling in living matter, numerical analysis of PDEs, scientific computing, scientific machine learning, and physics-informed methods. The work will address problems in mathematical oncology and cancer mechanobiology, combining mathematical modelling, numerical simulations, and experimental data in a strongly interdisciplinary setting with experimental collaborators.
Eligible candidates are encouraged to apply if they hold a PhD in Mathematics, Physics, Engineering, Computational Science, or a related field. A strong background in the listed modelling and computational areas is particularly relevant.
This is a postdoctoral opening; the post does not specify stipend, contract duration, or a formal deadline. Interested candidates should contact the professor directly for next steps.
Funding details
Postdoctoral position; funding details, salary, and contract length are not specified in the post.
What's required
Applicants should have a PhD in Mathematics, Physics, Engineering, Computational Science, or a related field. Strong background in partial differential equations, multiscale modelling in living matter, numerical analysis of PDEs and scientific computing, or scientific machine learning and physics-informed methods is preferred. Candidates should be highly motivated and able to work in an interdisciplinary environment with experimental collaborators.
How to apply
Interested candidates should contact Pasquale Ciarletta directly. The post does not provide a formal application portal or deadline.
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