Post-Doctoral position for Computational Molecular Sciences and Scientific Software Development
Pubblicato il 25-09-2026 - Aretè & Cocchi Technology in Genova
Post-Doctoral position for Computational Molecular Sciences and Scientific Software Development
Competenze, Esperienza, Qualifiche: se possiede i requisiti giusti per questa occasione, si candidi oggi stesso.
Project: Flagship program: Teaching science to computers
Commitment & contract: up to 2 years, collaboration contract
Location: Genoa, Via Enrico Melen 83
We are dedicated to providing equal employment opportunities and fostering diversity in all its forms, creating an inclusive environment. We value the unique experiences, knowledge, backgrounds, cultures, and perspectives of our people. By embracing diversity, we believe science can achieve its fullest potential.
The Role
You will be working in a multicultural and multi-disciplinary group, where junior and senior scientists collaborate, each with their expertise, to carry out a scientific activity with a shared research goal.
The Computational Modeling of Nanoscale and BioPhysical Systems – (CONCEPT) Research Unit is coordinated by Dr. Walter Rocchia,
who has extensive experience in biophysical modelling and computational sciences. The research focuses on development in the fields of molecular modelling and simulation of systems at the nanoscale, with relevant applications to health and biomedical sciences, especially protein design.
Responsibilities
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- Design, development, and implementation of novel computational methods at the interface between computational biophysics and machine learning, with a strong focus on scientific programming and algorithm development;
- Development and implementation of enhanced sampling techniques to accelerate molecular dynamics simulations of biologically relevant processes, including integration into existing simulation frameworks and custom software tools;
- Development, optimization, and application of computational methods for the description, characterization, and prediction of protein–protein and protein–nucleic acid interactions, with emphasis on robust, eff