Graph Algorithm Researcher - Virtual Patient Engine in Heidelberg bei BioMed X GmbH

About BioMed X

BioMed X is an independent research institute with sites in Heidelberg, Germany, New Haven, Connecticut, XSeed Labs in Ridgefield, Connecticut, and a worldwide network of partner locations. Together with our partners, we identify big biomedical research challenges and provide creative solutions by combining global crowdsourcing with local incubation of the world’s brightest early-career research talents. Each of the highly diverse research teams at BioMed X has access to state-of-the-art research infrastructure and is continuously guided by experienced mentors from academia and industry. At BioMed X, we combine the best of two worlds – academia and industry – and enable breakthrough innovation by making biomedical research more efficient, more agile, and more fun.

About the Team

The goal of team ‘Next Generation Virtual Patient Engine for Clinical Translation of Drug Candidates’ (VPE) led by Dr. Douglas McCloskey is to develop a versatile computational platform that can predict the efficacy of first- or best-in-class drug candidates in virtual patient populations at an unprecedented accuracy, thereby addressing one of the most critical bottlenecks of the pharmaceutical industry today: a 90% failure rate of new drug candidates during clinical development. In partnership with Sanofi the VPE team will develop innovative artificial intelligence methods to build the virtual patient platform. As a proof-of-concept, the initial platform will focus on chronic immune-mediated diseases such as atopic dermatitis (AD) and inflammatory bowel disease (IBD), where new medication that can address patient heterogeneity is needed.

The Position

We are looking for highly enthusiastic researchers to broaden our think-tank with their intellectual power and technical excellence. The ideal candidate would have a PhD degree or equivalent in artificial intelligence or related fields, and a strong background in modern deep learning techniques and working with large graph structured data.

Required Skills

• Proficiency in using and theoretical understanding of modern deep learning methods in deep graph modeling, and foundation model development and transfer learning.
• Experience in developing recommendation engines using static or temporal knowledge graphs, explanation models to debut and interpret recommendations, learned simulators of physical, biological, or other temporal data sources, and/or active learning or causal discovery using graphical models.
• Proficiency using modern deep learning libraries including PyTorch; using version control with Git, Docker containers, and Anaconda; using code management best practices such as unit testing, linting, and documentation; orchestrating machine learning experiments using cloud computing environments; and using continuous integration and deployment (CI/CD) frameworks.
• A strong interest in applying modern machine learning to solve problems in biology and medicine.
• Independent thinking
• Experienced to work in interdisciplinary teams
• Excellent communication skills in English

Additional Preferred Skills

• Experience working with biomedical knowledge graphs and ontologies.
• Experience working with -Omics data and in particular single cell sequencing data.
• Understanding of AGILE methodologies.

What We Offer

The post is offered for a limited term until June 30, 2028.

• Flexible working hours and hybrid working location
• Opportunities to publish in top academic journals and present at top academic and industry conferences.
• Training in how scientific teams take a high-risk and high-reward idea from development to early-stage productization using AGILE methodologies.
• Access to a vast network in science and industry.
• International, diverse, and positive work atmosphere that fosters personal and professional growth.
• Job ticket, sponsored fitness contract, complimentary fresh fruit, soft drinks, and chocolate team recognition events, free German lessons, complimentary Coursera courses, etc.

Candidates Are Requested to Submit

• 1-page cover letter explaining the reasons of interest to join our team and contributions you would make to the team.
• Curriculum Vitae outlining scientific interests, research achievements, and a record of publications.
• Two references will be asked for after submission as a part of the interview process.

Applicants will be reviewed and interviewed on a rolling basis. The position is available to fill starting February 1st, 2024. Please submit your application to t