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Modeling 3D genome structure from Hi-C data with deep learning
A Master thesis project at the Systems Biology group of IBM Research Zurich. Develop a deep-learning framework for inferring the 3D structure of genomes by extending our previous work using attention mechanisms, graph neural networks and transformers.
Keywords: deep learning, genome structure, systems biology
Please check full description (attached).
We invite applications from ETH/EPFL Master students with a background in Computer Science, Computational Biology, Bioinformatics or related fields. The ideal candidate should have a solid background in machine learning, deep learning and data analysis. Strong programming skills in Python and practical experience with at least one deep-learning framework (Tensorflow, PyTorch, Keras) are essential. Prior knowledge of molecular biology is not a prerequisite.
Not specified
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
Flexible
Flexible
Interested candidates are welcome to submit an application including CV and transcript of grades to Marianna Rapsomaniki: aap@zurich.ibm.com
Interested candidates are welcome to submit an application including CV and transcript of grades to Marianna Rapsomaniki: aap@zurich.ibm.com