IMPRINT is seeking a Computational Biologist/Machine Learning Scientist/Biostatistician to develop novel mathematical, simulation, and machine-learning methods to advance the understanding of adaptive immune receptor repertoires.
Requirements
- Proficiency with Python, associated analyses and visualization packages, as well as command line
- Proficiency in high-performance computing
- Proficiency with good practices for reproducible research (git, Jupyter)
- Background in machine learning
- Expertise with sc-RNAseq data
- Expertise in structural models
- Experience with cloud computing platforms
Responsibilities
- Develop mathematical, computational, and machine learning approaches for modeling large-scale immune receptor sequence and/or structural data
- Develop novel simulation frameworks for immune repertoire analysis
- Perform deep and detailed analyses for data-intensive experiments
- Prepare large-scale datasets for internal and external usage
- Expand own knowledge base with new methods and concepts to tackle evolving research questions and demands for collaboration
Other
- Ph.D. in computational biology, physics, mathematics, statistics, computer science, or related fields is required
- Proven track record of developing computational analyses for biological data
- International travel as needed for team meetings and other business purposes
- Work remotely or hybrid from within Europe or the US, depending on candidate location
