Scientist building ensemble-aware protein representations and ML models that integrate dynamic structural data with PLMs, ligands, and functional info to advance dynamic structural biology. Requires PhD and experience with large bioinformatic pipelines.
Lead development of computational infrastructure for large-scale neuroscience research, building real-time stimulus/acquisition systems, high-throughput data pipelines, and cloud tools. Requires exceptional full-stack engineering with real-time systems experience; neuroscience background not required.
Open general application for computational biology, ML research, data science, software engineering, and program roles at DiffUSE, focused on protein dynamics, structural data infrastructure, and open science tooling.
Leads technical direction for bio AI research programs, architects AI/computational infrastructure, prototypes modeling systems, and builds high-caliber engineering team. Requires deep ML expertise in generative models, large-scale training, and bio applications with hands-on leadership.
Builds high-performance infrastructure for brain reverse-engineering via large-scale AI models and neuroscience-informed simulations. Architects systems, optimizes RL environments, and scales research prototypes to production.
Scientist building ensemble-aware protein representations and ML models that integrate dynamic structural data with PLMs, ligands, and functional info to advance dynamic structural biology. Requires PhD and experience with large bioinformatic pipelines.
Lead development of computational infrastructure for large-scale neuroscience research, building real-time stimulus/acquisition systems, high-throughput data pipelines, and cloud tools. Requires exceptional full-stack engineering with real-time systems experience; neuroscience background not required.
Open general application for computational biology, ML research, data science, software engineering, and program roles at DiffUSE, focused on protein dynamics, structural data infrastructure, and open science tooling.
Leads technical direction for bio AI research programs, architects AI/computational infrastructure, prototypes modeling systems, and builds high-caliber engineering team. Requires deep ML expertise in generative models, large-scale training, and bio applications with hands-on leadership.
Builds high-performance infrastructure for brain reverse-engineering via large-scale AI models and neuroscience-informed simulations. Architects systems, optimizes RL environments, and scales research prototypes to production.