Student for improving predictive models with additional …, Groningen

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:

Internship Responsibilities

Access 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 the similarity of the transcriptomes.

Add the synthetic transcriptomes to the predictive model training and retrain.

Compare and characterize the 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.

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

Candidate Profile

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.

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