Student for improving predictive models with additional synthetic transcriptomes

Join a translational AI-oncology project that uses atlas-scale bulk, single-cell and spatial transcriptomic data to generate synthetic transcriptomes as realistic as possible and improve predictive models of cancer response.

What will you do?

In this internship you will contribute to a computational proof-of-concept that improves predictive model performance by using additional synthetic transcriptomes.

Your activities may include:

• Accessing the already harmonised 800,000 bulk, over millions single-cell and >1,000 spatial transcriptomic datasets
• Collect and gather immunotherapy response datasets which have transcriptomes along with clinicopathological information
• Obtain the performance of the models with the transcriptomes present currently
• Use state-of-the-art generative models to generate realistic synthetic transcriptomes for each context and measure transcriptome similarity
• Add the synthetic transcriptomes to the predictive model training and retrain
• Compare and characterize performance with and without additional synthetic transcriptomes
• Evaluate model outputs for robustness, biological plausibility and reproducibility across cohorts
• Visualise results and translate findings into a scientific report and/or manuscript
• Contribute to reproducible code, documentation and analysis pipelines

What is this research about?

You will work within UMCG on a multidisciplinary project at the intersection of medical oncology, computational immunology, spatial transcriptomics and AI. The project builds on unique harmonised datasets from endometrial cancer, including bulk, single-cell and spatial transcriptomic profiles, and is embedded in a network spanning oncology, pathology, molecular biology and biomedical AI.

Not all cancer patients respond to immunotherapy treatments, and predictive models often perform poorly due to low sample sizes in rare cancers. This project asks whether generating realistic synthetic transcriptomes can help improve predictive model performance.

Whom are we looking for?

You are a MSc or advanced BSc student in bioinformatics, computational biology, AI, data science, biomedical sciences, mathematics or a related field.

• Experience with Python and/or R for data analysis
• Affinity with machine learning, statistics or generative modelling
• Interest in cancer immunology, transcriptomics or spatial omics
• Ability to work with large and heterogeneous datasets
• Strong analytical thinking and problem-solving skills
• Clear written and verbal communication skills
• Independent, curious and motivated, while also enjoying collaboration in a multidisciplinary team

What we offer

• Internship agreement with UMCG
• Intensive supervision in a multidisciplinary research environment
• Opportunity to work on a novel AI and computational immunology project with atlas-scale data