We are seeking a highly motivated postdoctoral researcher with a strong computational background to join our research project on protein-protein interaction (PPI) networks. Our work addresses critical challenges in network biology. This project has a focus on enhancing the reliability and interpretability of protein-protein interaction data. You will be joining a vibrant international team at one of Europe’s leading cancer research institutes.
Project overview:
Protein-protein interaction networks are key to understanding biological and disease processes, but current methods to detect, integrate and analyze those networks suffer from high technical error rates and biases, especially toward frequently studied proteins. In a joint project with the Friedrich Alexander University (Erlangen, Germany) and the Technical University of Munich (Germany), we aim to improve the quality of PPI networks by leveraging large scale experimental information towards two key objectives:
- Developing a negative gold standard for PPI prediction: We will create a robust set of pairs of proteins that are highly unlikely to interact, providing essential negative examples for machine learning models.
- Mitigating the study bias in PPI networks: We will design and implement algorithms to reduce the impact of false positives caused by the over-testing of certain proteins, ultimately improving the accuracy and reliability of PPI networks.
What we offer:
- World-class research environment: The IEO, located on the IFOM-IEO campus in central Milan, is renowned for its excellence in cancer research, with a strong emphasis on genomics, clinical applications, and the mechanistic understanding of cancer biology.
- Collaborative and diverse team: You’ll be part of a dynamic, interdisciplinary community that fosters collaboration between computational and experimental scientists. The candidate will work closely together with Ph.D. students and postdocs in Erlangen and Munich.
- State-of-the-art infrastructure: Access to excellent HPC infrastructure and core facilities, ensuring you have the resources to excel in your research.
- Learning opportunities: You will receive close supervision and mentorship, with significant opportunities to expand your expertise in network biology, machine learning, and programming.
Requirements:
- PhD in Computational Biology, Bioinformatics, Computer Science, or a related field.
- Background in machine learning and network biology.
- Proficiency in programming languages (e.g., Python, R).
- Experience with large-scale data analysis and high-performance computing.
- Enthusiasm for learning and applying new computational methods to biological questions.
How to apply:
Interested candidates should send a CV, cover letter, and the contact details of at least two referees to [email protected]. Also informal queries are very welcome and more information will be provided. Applications will be reviewed on a rolling basis until the position is filled.
Posted on 3rd October 2024