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Designing metrics for tumor profiling based on spatial, multi-modal molecular data
Develop a computational framework to characterize tumor heterogeneity in a spatial manner from multi-modal omic data
Keywords: data integration, machine learning
The primary objective of this project is to develop a computational framework to characterize heterogeneity in a spatial manner. To achieve this, the candidate will integrate multimodal data from spatial molecular assays and generate novel metrics. The developed metrics will have the potential to aid patient stratification and comparative clinical studies.
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 and data analysis. Strong programming skills in Python and practical experience with state-of-the-art ML libraries are essential.
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.
Duration: 6 months, flexible starting date
Duration: 6 months, flexible starting date
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).