Postdoc in computer-aided structure-based drug design and md simulations
Pubblicato il 03-10-2026 - Macchinari e impiantistica in Italia
Postdoc in computer-aided structure-based drug design and md simulations
Italy
Commitment & contract: 1-year, renewable collaboration contract
Location: Genova, Via Enrico Melen 83
Step into a world of endless possibilities, together let's leave something for the future
At IIT, we are committed to advancing human-centered Science and Technology to address the most urgent societal challenges of our era. We foster excellence in both fundamental and applied research, spanning fields such as neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific experience. Our approach integrates cutting-edge tools and technology, empowering researchers to push the limits of knowledge and innovation. With us, your curiosity will know no bounds.
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
The Molecular Modeling and Drug Discovery Research Unit is coordinated by Dr. Marco De Vivo, who has extensive experience in computational and medicinal chemistry for drug discovery.
The De Vivo lab is a multicultural and multi-disciplinary group, where you will be working in close contact with both computational and experimental researchers in medicinal chemistry, biochemistry and pharmacology.
Good candidates are scientists with expertise in computational methodologies for structure-based drug design, like molecular docking and virtual screening methods and software, molecular dynamics and free energy perturbation calculations for drug design.
The research project is centered on developing and applying modeling and simulations (coupled to machine learning when possible) to characterize and design small molecu