Offer summary

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 and proficiency with Python and associated visualization packages., Experience with high-performance computing and good practices for reproducible research..

Key responsabilities:

Develop mathematical, computational, and machine learning approaches for modeling large-scale immune receptor data.

Create novel simulation frameworks for immune repertoire analysis.

Conduct deep analyses for data-intensive experiments and prepare datasets for various uses.

Continuously expand knowledge with new methods to address evolving research questions.

Job description

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.

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.

Required profile