PhD - Deep Learning and Perturbation Models (f/m/x) at He...

Intro

Join Helmholtz Munich as a Doctoral Researcher in a collaborative project with Pfizer Inc., focusing on deep learning and perturbation models for target discovery and precision medicine.

Tasks

Curate, prepare, and analyse immune cell CRISPR data.
Integrate large-scale clinical omics data and meta data from public and private clinical studies.
Support with the development of deep learning algorithms to compare human diseases and preclinical models.
Model both genetic and chemical perturbations in out-of-distribution conditions.

Requirements

Major in one of the biological sciences, computer science, math, physics, chemistry, etc.
Proficiency in programming languages such as Python
Experience in analysing and integrating large-scale omics data (e.g., RNA-seq, bulk transcriptomics, single-cell data) and applying machine learning methods
Ability to work collaboratively in a multidisciplinary team
Interest in obtaining a Ph.D. in Computational Biology with a focus on machine learning, deep learning, or artificial intelligence.
Research experience in developing, applying, and evaluating deep learning algorithms (preferred)
Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) (preferred)
Familiarity with perturbation models, genetic perturbations, and compound data (preferred)
Good publication record (preferred)

Benefits

Scientific Training: Helmholtz Munich Graduate School
Career counseling: Career Center for PhD candidates
Recreation: 30 days annual leave, flexi days, plus public holidays
Health Promotion: Sports, company doctor, mental health initiatives
International Staff Service: Support with the relocation and integration process in Germany

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