
Data Scientist in Cancer Research
2 days ago
Cancer Data Science Opportunities
We are seeking a highly motivated postdoctoral researcher to join our team at the Faculty of Medicine, University of Helsinki. The successful candidate will have the opportunity to work on cutting-edge data science projects funded by the iCAN Flagship in Digital Precision Cancer Medicine.
As a postdoctoral researcher, you will be part of a dynamic, interdisciplinary research environment collaborating with experts in computational biology, cancer research, and precision medicine. Our group is embedded in iCAN and connected to an active and welcoming computational biology community across the Nordics, providing opportunities for networking, training, and collaboration.
The advertised project focuses on implementing a hierarchical Variational Autoencoder (VAE) framework to integrate bulk multi-omics cancer data. Developed by the Kuijjer group to integrate multi-modal single-cell data, this framework incorporates biological data dependencies to model variability at both joint and data type-specific levels, providing insights into regulatory mechanisms. The research will leverage a large in-house dataset from iCAN, complemented by publicly available data, to examine transcriptomic dependencies on transcription factor abundance and/or somatic mutations.
Key Responsibilities
- Implementing a hierarchical VAE framework to integrate bulk multi-omics cancer data
- Developing novel computational approaches to analyze large-scale multi-omics datasets
- Contributing to research with direct relevance to human health
Required Skills and Qualifications
To be eligible for this position, candidates should have a PhD degree in computational biology, bioinformatics, computer science, biostatistics, cancer genomics, network science, or related fields. Proficiency in programming, experience with high-performance computing and GPU acceleration tools (e.g., CUDA), and deep learning frameworks such as TensorFlow or PyTorch is a requirement for this position. Experience with VAEs or related frameworks, statistical analysis of high-throughput omics data, comparative genomics, functional genomics, or related fields is a strong advantage.
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