Life Sciences

Area/s: Life Sciences

Organization: Josep Carreras Leukaemia Research Institute

Research theme code : RLA-IRJC-01

Principal Investigator: This opportunity is open to all IRJC researchers. For a comprehensive list of research groups and Principal Investigators, please visit the following link: IRJC Research Groups and Principal Investigators.
Email: internationalgrants@carrerasresearch.org
Web: https://www.carrerasresearch.org/en

Brief Theme Description:

Multidimensional high-content data, including imaging, transcriptomics, and genomics—ranging from bulk to single-cell and spatial resolution—hold transformative potential in cancer management. These data modalities can enhance diagnosis, improve patient monitoring, and inform therapeutic decisions, paving the way for precision oncology.
The IRJC is offering two postdoctoral fellowships to support innovative research in this field. Fellows will leverage artificial intelligence tools, including deep learning and machine learning, to analyse donor-derived datasets in health and disease. By uncovering complex patterns in these data, we aim to advance understanding of cellular and molecular interactions driving cancer development, ultimately contributing to precision medicine.
Candidates are required to submit research project proposals as part of their application. These proposals should align with the overarching goals of identifying novel biomarkers for targeted therapies and developing predictive models for disease progression, treatment monitoring, and therapy outcomes. By integrating state-of-the-art technologies with AI methodologies, proposed projects should aim to advance personalized oncology and improve patient care and quality of life.
If you are a highly motivated researcher eager to explore the intersection of multidimensional data, AI, and oncology, we encourage you to apply for one of these exciting fellowships. Join us in pushing the boundaries of cancer research and transforming patient outcomes.

Possible Secondments: To be decided, depending on the Principal Investigator.
Keywords: Spatial Transcriptomics; Single-cell Genomics; Machine Learning; Precision Oncology.