Beschreibung

You design and benchmark novel machine learning algorithms for complex biological problems, working at the intersection of causal representation learning and drug discovery.

Tasks

• Design, implement and benchmark ML algorithms using causal representation learning and out-of-distribution generalization
• Frame foundational research questions beyond off-the-shelf ML paradigms for drug discovery
• Collaborate with cross-functional research teams across global innovation hubs
• Build and scale neural network architectures using deep learning frameworks
• Translate biological challenges into rigorous computational models
• Author scientific publications for top-tier ML and computational biology venues
• Present research findings at internal symposia and international conferences

Profile

• Ph.D. (or exceptional Master's degree) in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics or related field
• Solid grounding in machine learning theory, with interest in causal representation learning or out-of-distribution generalization
• Hands-on expertise building and training deep neural networks, proficiency with modern deep learning frameworks
• Track record of publications at premier ML conferences or journals
• Interest in working with biological datasets and molecular systems
• Strong collaborative and interpersonal skills across distributed global teams

Company

Pharmaceutical research