Lead ML Scientist: Causal DL for Drug Discovery

Try&Hire; Basel; On-site; CHF 120,000 - 180,000; Full time

Job summary

Try&Hire in Basel, Switzerland designs and benchmarks novel ML algorithms for complex biological problems at the intersection of causal representation learning and drug discovery. You will design, implement and benchmark ML models using causal ideas, frame foundational questions beyond off-the-shelf methods, and collaborate with cross-functional teams across global innovation hubs.

You will publish and present your findings at top ML and computational biology venues.

Qualifications

• Ph.D. (or exceptional Master’s) 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.

Responsibilities

• 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.

Skills

ML theory

Deep learning

Causal representation learning

Publications

Collaboration

Biological data

Education

Ph.D. in CS/Statistics/Math/Computational Biology

Tools

PyTorch

Job description

Try&Hire in Basel, Switzerland designs and benchmarks novel ML algorithms for complex biological problems at the intersection of causal representation learning and drug discovery. You will design, implement and benchmark ML models using causal ideas, frame foundational questions beyond off-the-shelf methods, and collaborate with cross-functional teams across global innovation hubs.

You will publish and present your findings at top ML and computational biology venues.