Computational Immunologist/ Machine Learning Scientist/ Biostatistician
Remote (DE)
IMPRINT is a biomedical Focused Research Organization (FRO) studying the hidden causes of chronic disease, especially autoimmune and psychiatric disorders. Our work involves cutting-edge research at the intersection of immunology, neuroscience, and behavior.
We are looking for a Computational Biologist/Machine Learning Scientist/Biostatistician with experience in the computational modeling and analysis of large-scale biological datasets, preferably with expertise in structural models or experience in user interactions. The successful candidate will develop novel mathematical, simulation, and machine-learning methods to advance the understanding of adaptive immune receptor repertoires. You will work closely with other computational and experimental scientists to develop groundbreaking new insights into adaptive immunity.
It would be ideal for the candidate to be based in Germany or elsewhere in Europe, since the majority of the computational team is based there, but we are open to exceptional candidates in the US and Canada.
We are looking for a Computational Biologist/Machine Learning Scientist/Biostatistician with experience in the computational modeling and analysis of large-scale biological datasets, preferably with expertise in structural models or experience in user interactions. The successful candidate will develop novel mathematical, simulation, and machine-learning methods to advance the understanding of adaptive immune receptor repertoires. You will work closely with other computational and experimental scientists to develop groundbreaking new insights into adaptive immunity.
It would be ideal for the candidate to be based in Germany or elsewhere in Europe, since the majority of the computational team is based there, but we are open to exceptional candidates in the US and Canada.
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.
Job Profile
Benefits/PerksCompetitive compensation Excellent medical insurance Generous time off Opportunity for publications Supportive learning environment
Tasks- Analyze large datasets
- Create simulation frameworks
- Develop mathematical models
- Expand knowledge base
- Prepare datasets
Biostatistics Cloud Computing Collaboration Computational Biology Data analysis Docker Git High Performance Computing Immunology Jupyter Machine Learning Python Sc-rnaseq data Structural models
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