Postdoctoral Researcher, Experimental Data Generation & CRO Strategy
1.Experimental campaign design, including areas such as Design scalable campaigns for biomolecular interactions, conformational dynamics and related protein measurements. Select systems, constructs, assays and controls based on scientific value, feasibility, diversity, throughput and cost. Anticipate bottlenecks and define success criteria, contingency plans and follow-up experiments.
Diagnose artifacts, failed fits and systematic assay problems using quantitative and biophysical reasoning. Work with computational researchers to prioritize systems, evaluate model predictions and design informative follow-up experiments. Contribute to the experimental data strategy for future BioEmu models. Translate research goals into clear work packages, milestones and experimental requirements.
Coordinate parallel programs with CROs and academic collaborators, review progress and guide corrective iterations. Provide scientific direction on protein production, assay development and biophysical or structural characterization.
PhD in Biology, Biophysics, Biochemistry, Molecular Biology, Protein Science, Bioengineering, Computational Biology, Molecular Modelling, or a related field, with experience designing, conducting, or analyzing biomolecular experiments and/or computational studies. Strong quantitative understanding of experimental measurements and their limitations.
Ability to coordinate complex projects and communicate clearly across experimental and computational teams. Experience working with real-world biological, structural or biophysical datasets. Ability to independently own and deliver research projects. Experience managing CROs, vendors or distributed experimental collaborations. Expertise in protein-protein interactions, binder design, affinity optimization or high-throughput assay development.
Familiar with techniques such as protein expression and purification, binding assays (SPR, BLI, ITC, cryo-EM), structural biology (X-ray crystallography, NMR), Mass Spec (HDX-MS, Cross-link Mass Spec). Practical Python or equivalent scripting skills for data analysis, QC and workflow automation. Experience in designing, curating or standardizing datasets for machine-learning applications.
Interest in model-guided experimental design, drug discovery or therapeutic applications.
