PhD Position (all genders) in Computational Biology and Deep Learning for Spatial Omics
Core
Develop computational and machine learning methods to analyze and integrate spatial transcriptomics data, characterize tissue organization, and derive data-driven molecular heterogeneity of kidney disease.
Role type
PhD researcher in computational biology and deep learning
Builds
Open-source software for spatial omics analysis and deep learning models
Domain
Biomedical research, spatial transcriptomics, kidney disease
Deliverable
production ML models
Required skills
Python programming, deep learning frameworks (PyTorch), statistics, machine learning, geometric/topological deep learning, graph neural networks
Preferred skills
spatial transcriptomics data analysis, computational pathology, single-cell data analysis
Technologies
PyTorch, scientific Python ecosystem, bAIome HPC infrastructure
Responsibilities
Develop and release open-source software, apply methods to human kidney biopsy cohorts, present results at international conferences, publish in peer-reviewed journals
Seniority
PhD candidate (Junior Researcher)