Phd Student - Machine Learning For Biosystems Engineering
Roche
Basel, Switzerland
Not specified
Master's degree in computational biology or computer science
Proficiency in python programming language
Experience with jax pytorch or tensorflow frameworks
Roche is seeking a PhD student for its Machine Learning for Biosystems Engineering group in Basel, Switzerland. The role involves developing advanced machine learning methods for organoid phenotyping and drug discovery, requiring a strong background in computational biology or related fields
Job Summary
The role involves leading a research project to develop computational methods for organoid engineering and drug discovery within the Institute of Human Biology.
Candidates will work with rich, high-content datasets from complex human model systems including large-scale perturbation experiments with multi-modal readouts.
The position offers opportunities to publish work, contribute to open-source tools, and gain exposure to drug discovery processes while collaborating with experimental scientists.
Matching Summary
Match Score: 85
Roche is seeking a PhD student for its Machine Learning for Biosystems Engineering group in Basel, Switzerland. The role involves developing advanced machine learning methods for organoid phenotyping and drug discovery, requiring a strong background in computational biology or related fields.
Skills & Requirements
Must-have
Master's degree in computational biology or computer science
Proficiency in Python programming language
Experience with JAX PyTorch or TensorFlow frameworks
Strong fundamentals in linear algebra and statistics
Familiarity with version control GitHub GitLab
Nice-to-have
Track record of relevant publications in ML
Experience applying ML to biomedical genomics data
Background in single-cell genomics analysis
Experience with image analysis and computer vision
Proven ability to collaborate with experimental scientists
Key Requirements
Master's degree or recent graduate status required
Proficiency in modern software engineering methodologies