AI for Science Postdoctoral Researcher - Biomolecular AI & Experimental Data Integration
Core
Design and scale experimental datasets for ML and develop workflows connecting noisy experimental signals to actionable model insights.
Role type
Postdoctoral Researcher (Biomolecular AI & Experimental Data Integration)
Builds
Automated, reproducible pipelines for data ingestion, processing, and analysis; closed-loop workflows where experimental results refine models.
Domain
Biomolecular systems, structural biology, drug discovery, therapeutics
Deliverable
production ML models
Required skills
Python, machine learning for biomolecular systems, molecular modeling and simulation, statistical mechanics, data curation, uncertainty estimation, cryo-EM, X-ray, NMR, SPR, mass spectrometry, NGS, generative models, diffusion models, representation learning, molecular dynamics
Preferred skills
Experience with large-scale dataset generation, automated analysis workflows, protein expression, purification, interaction assays, high-throughput systems, drug discovery applications
Technologies
Python
Responsibilities
Design high-quality, ML-ready experimental datasets; establish closed-loop workflows; build automated pipelines; develop systems for data QC and uncertainty estimation; provide technical guidance on experimental design; contribute to novel methods at the model-experiment interface.
Seniority
Postdoctoral Researcher