Computational Biologist, Synthetic Spatial Omics
Tasks
- Build predictive models for molecular states and functional outcomes
- Contribute to computational design or evaluation of synthetic receptors transcription factors and protein circuits
- Create preprints publications presentations and open science artifacts
- Design studies and define quantitative benchmarks with experimental scientists
- Develop machine learning approaches to identify interpretable biological principles
- Develop reproducible scalable analysis pipelines and software
- Lead computational analysis of multiplexed imaging and spatial omics datasets
Perks/Benefits
- 401k match
- Family forming benefits funding
- Hybrid work onsite 60 percent of the time
- Paid time off volunteer
- Relocation support
Skills/Tech-stack
Cell analysis | Cell biology | Cell segmentation | Computational protein design | Data Analysis | Deep learning | Genomics | Image Segmentation | Image analysis | Machine Learning | Morphological Profiling | Multi-Omics | Multi-Omics Analysis | Omics Analysis | Protein Design | Python | R | Representation Learning | Single Cell Analysis | Single cell data | Single-Cell Data Analysis | Single-cell | Spatial Statistics | Spatial omics | Subcellular Image Analysis | Synthetic biology | Transcriptional Regulation
Education
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