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Postdoc in machine learning for cancer

  • 27 Feb 2024
  • SISSA, Trieste

JOB: postdoc in machine learning applied to cancer @SISSA, Trieste

Dear all

we have an opening for one 2-years (renewable) postdoctoral position working on machine learning for hematological cancer data in our group at the International School of Advanced Studies in Trieste. Essential: knowledge of statistical/ machine learning methods applied in a scientific context. Desirable: familiarity with bioinformatics methods for sequencing data. Applications from women and under-represented groups are particularly welcome. Please email me before making an application if you have any questions.

Deadline and link: 27/02/2024 


About the job: We have recently obtained a substantial grant from AIRC, the Italian cancer research charity. The project is in collaboration with Humanitas University Hospital in Milan and the University of Bologna, and aims to integrate diverse data types (clinical, genomic, transcriptomic and epigenomic) to support clinical decision making, particularly in data poor situations. The approach combines Bayesian methods and advanced ML techniques (GNNs, VAEs etc) as well as methods from interpretable AI.

About SISSA: SISSA is an international PhD school located in Trieste, north-eastern Italy. It hosts about 100 PIs and 500 researchers (PhD and postdocs) in an interdisciplinary setting focussed on physics, mathematics and neuroscience. SISSA established a data science group in 2020 working on methodological and scientific applications of machine learning, see here https://datascience.sissa.it.

About Trieste: Trieste is a city of about 200K inhabitants located on the northernmost point of the Mediterranean Sea, close to the border with Slovenia. Long the port city of the Austro-Hungarian empire, it is a beautiful city combining a mediterranean and Central European character. It is regularly listed as one of the top Italian cities in terms of quality of life, with easy access to the Alps, the Adriatic Sea, and a relatively low cost of living.

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