As authors of the FLOWR and PILOT frameworks, we are expanding our group of machine learning research scientists to further advance the development and application of state-of-the-art generative models for both structure- and ligand-based drug design. * Design, develop, and validate state-of-the-art machine learning models, with a focus on generative AI and self-supervised learning * Develop predictive models combining structural and biochemical data (e.g., binding affinity prediction) * Proven experience in developing machine learning models and algorithms * Hands-on experience implementing deep learning models using frameworks such as PyTorch * Expertise in modern generative modeling techniques, such as diffusion models, flow-matching approaches, reinforcement learning and/or self-supervised learning methods (e.g., JEPA)
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