
Researcher in Machine Learning and Genomics
5 days ago
Research Group Overview
The Cancer Data Science group led by Mariike Kuijjer at iCAN Flagship in Digital Precision Cancer Medicine, Department of Biochemistry and Developmental Biology, at the Faculty of Medicine, University of Helsinki, specializes in developing computational methods to understand the regulatory mechanisms driving cancer heterogeneity and progression by integrating genomic data into genome-wide regulatory networks.
About the Role
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.
Qualifications and Requirements
- 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)
- Deep learning frameworks such as TensorFlow or PyTorch
How to Apply
Please submit your application as a single pdf file, comprising the following documents in English:
- Motivation letter outlining your previous knowledge and research interests, career goals, your motivation of applying to this specific position, and your expectations of the position
- Complete Curriculum Vitae, including a list of publications and 2-4 referees
- Certificates/Diplomas: Scanned copies of your PhD diploma
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