About the Project
Here we plan to harness recent advances in deep learning and network theory to identify the regulatory networks controlling these processes. We will then analyse publicly available datasets, including those generated by the Ly lab, to identify regulatory networks that can be validated in functional cells.
The student will develop new methods for network discovery based on deep learning, using graph neural networks and dimensionality reduction techniques, such as graph embedding and autoencoders. He/she will also receive training in writing reproducible bioinformatics analysis workflows, mass spectrometry-based analysis of cellular proteins and high-quality research software.
The ideal candidate has a background in mathematics, computer science, computer engineering, statistics, physics or related fields. He/she is strongly motivated to develop a competitive profile in machine learning, data science and computational biology and likes to work in a fast-pace environment.
If you would like us to consider you for one of our scholarships you must apply by 5 January 2020 at the latest.
2. Fanfani, V., & Stracquadanio, G. (2019). A unified framework for geneset network analysis. doi:10.1101/699926
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