Primary 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
Qualifications
- 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
- Background in machine learning
- 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)
Preferred Qualifications
- Expertise with sc-RNAseq data
- Expertise in immunology
- Expertise in immune receptor biology and data analysis
- Expertise in structural models
- Experience with cloud computing platforms
- Experience with Docker
- Experience with user interactions
Benefits
- Competitive compensation: $90,000 - $120,000 a year (salary commensurate with relevant experience and adjusted for location)
- Excellent medical, dental, and vision insurance for US- based candidates; benefits for INTL employees vary by country
- Generous time off + paid holidays
- A supportive environment to learn and develop new skills
- An opportunity to participate in high-impact, fast-paced, cutting-edge, collaborative team science; contribute to curing disease; and work with leading experts from different fields
- The opportunity to contribute to scientific publications
Location
- Work remotely or hybrid from within Europe or the US, depending on candidate location. Will require international travel as needed for team meetings and other business purposes.