Descripción The Spanish National Cancer Research Centre (CNIO) is one of the top leading cancer research institutes in Europe, hosting world-renowned scientists that have made significant contributions in both basic and translational aspects of cancer biology.
The Confocal Microscopy Unit is focused on the implementation of a wide range of advanced fluorescence microscopy technologies to bring CNIO research to the state-of-the-art in imaging and image analysis.
We aim to expand our image analysis platform by adding Artificial Intelligence (AI) tools for image segmentation and downstream analysis of imaging data and facilitate the data interpretation at the single cell level and explore the integration with other biological datasets including spatially multi-omics and clinical data.
The development of these tools represents a transformative leap towards a deeper and more integrated understanding of cancer biology, improve patient stratification, and enhance therapeutic strategies.
We are seeking a highly skilled candidate with experience in Bioimage Analysis and Data Mining to develop and apply advanced AI methods in analysing biomedical images.
This role involves building AI models for interpreting complex biological data, extracting insights from biomedical images, including microscopy and histopathology data and integrating bioimage analysis with other large-scale biological datasets (e.g., genomics, proteomics).
The ideal candidate will have expertise in deep learning and data mining with a focus on bioinformatics or biomedical applications.
Confocal Microscopy Unit (CMU) is currently starting to implement AI methods for image segmentation from a variety of microscopy images including histology and organoids samples.
As mentioned before, this has resulted in great improvement of segmentation accuracy improving the results at the single-cell level.
However, CMU is lacking the expertise to enhance the feature extraction and the downstream analysis trough the AI for data visualization and interpretation.
And therefore, CMU is seeking for a candidate to bring this expertise and to develop these specific tools.
This project will also try to integrate data from other technologies such a spatial transcriptomics offering a comprehensive understanding of tissue architecture and molecular characteristics.
"La financiación de estos contratos, procede del Mecanismo de Recuperación y Resiliencia de la Unión Europea-Next Generation, en el marco de la Invitación General de la entidad pública empresarial Red.es para participar en los programas de atracción y retención del talento dentro de la Inversión 4 del Componente 19 del Plan de Recuperación, Transformación y Resiliencia."Criterios de evaluación• MSc or PhD degree in Life Science, Computer Science, Bioinformatics, Physics, Engineering or Maths.
• Previous experience in biomedical data analysis proven by article publication.
• Proven experience in analysing bioimages using AI techniques.
• Experience with data mining techniques applied to biological data (e.g., clustering, spatial analysis, neighbourhood analysis, association rule mining, dimensionality reduction).
• Proven experience in collaborative data analysis projects.
• Good knowledge in Python or other relevant languages and processing libraries related to 2D and 3D image analysis (Python, Java, Groovy, Matlab).
• Good understanding of software engineer and high-performance computing environments and cloud platforms.
• Good knowledge of Machine and Deep Learning frameworks and the ability to train and generate models for cell segmentation in microscopy images including histology data (Cell pose, Stardist, Napari, Tensorflow, Pytorch).
• Familiarity with multi-omics data analysis (genomics, transcriptomics, proteomics) and integration with imaging data.
• High level of English, both written and spoken.
• Capacity for teamwork and motivation.
• Applications must include a detailed CV, synopsis of work experience and at least two reference letters.Se ofrece– The opportunity to be part of one of the few European Cancer Research Centres of excellence.
– An excellent working, multidisciplinary environment.
– Competitive salary.
– Contract linked to a project.
