SI/ML Computational Biologist - Postdoctoral Researcher
Core
Conduct research at the intersection of super intelligence (SI), machine learning (ML), and computational biology to understand host-response dynamics in complex biological systems using large-scale multiomic datasets.
Role type
Postdoctoral Research Staff Member (Research Scientist)
Builds
Predictive models and biologically meaningful insights from diverse omics datasets
Domain
Computational Biology / Machine Learning / Multi-omics
Deliverable
production ML models
Required skills
Machine learning, statistical modeling, computational biology, Python, R, PyTorch, TensorFlow, Linux/Unix, high-dimensional biological data analysis
Preferred skills
Deep learning, probabilistic modeling, single-cell/spatial/multimodal omics data, multiomic data integration, transfer learning, transformers, self-supervised learning, immunology
Technologies
PyTorch, TensorFlow, scVI, transformers
Responsibilities
Develop and apply machine learning methods for prediction and representation learning; Design and implement workflows for integrative analysis of multiomic datasets; Investigate approaches for multimodal data fusion and cross-dataset integration; Train and evaluate self-supervised and foundation models; Develop interpretable models linking molecular states to disease trajectories; Process and analyze large-scale sequencing and omics datasets; Publish results in peer-reviewed journals
Seniority
Postdoctoral (Early career, mentored training)
