AI for Science Postdoctoral Researcher – Biomolecular AI & Experimental Data Integration  

Design and scale experimental datasets for ML Develop workflows that connect noisy experimental signals to actionable model insights 1. Bridging Models with Real-World Experimental Signals Develop methods to connect ML models with experimental observables, such as: cryo-em density maps inding affinity / kinetics assays proteomics / sequencing data Design high-quality, ML-ready experimental datasets (e.g., protein interactions, conformational dynamics, binding measurements, cryo-em density). Establish closed-loop workflows where experimental results refine models and vice versa. Build automated, reproducible pipelines for data ingestion, processing, and analysis (Python-based). Develop systems for data curation, QC, and uncertainty estimation on noisy experimental data. Provide technical guidance on experimental design, data quality, and iteration cycles. Contribute to novel methods at the model-experiment interface. Completed or nearly complete PhD or equivalent experience in a science or engineering discipline. Deep expertise in at least one relevant area, such as machine learning for biomolecular systems, molecular modeling and simulation, structural biology, experimental protein assays, or statistical mechanics. Strong Python skills and experience building data analysis, modeling, or machine learning pipelines. Experience working with real-world biological, structural, experimental, or molecular datasets. Ability to work across disciplines and communicate complex ideas clearly. Track record of independently owning and delivering research projects. Experience connecting computational models to experimental data, such as cryo-EM, X-ray, NMR, SPR, mass spectrometry, NGS, or other assay readouts. Background in generative models, diffusion models, representation learning, molecular dynamics, or statistical mechanics for biomolecular systems. Experience with large-scale dataset generation, curation, or automated analysis workflows. Familiarity with experimental workflows such as protein expression, purification, interaction assays, or high-throughput systems. Interest in closing the loop between modeling and experiment. Experience or interest in drug discovery, therapeutics, or real-world biomedical applications. Ability to collaborate with external partners and align research goals with practical health challenges.