Data selection and quality evaluation for biological foundation models

Role: Computational biologist specializing in experimental design and data analysis to support AI-driven drug discovery. Objectives: improve biological datasets, understand measurement artifacts, and enhance model training through experimental insights. Responsibilities: collaborate with biologists and machine learning researchers to design and analyze large-scale biological experiments, identify sources of bias and variability, and communicate findings to facilitate data-driven decision-making. Requirements: PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, or biophysics; proven experience analyzing complex biological datasets and translating findings into experimental strategies; strong programming skills in Python; excellent communication skills. High-Value: focus on biological data quality and experimental design for AI models, cross-disciplinary collaboration, global team coordination, and work from a U.S. office with travel several times annually.