Research Engineering/ Scientist Assistant - Deep Learning for Structural Biology

The selected candidate will be involved in the development, implementation, and evaluation of deep learning methods for structural biology and molecular modeling. Depending on experience and project needs, responsibilities may include:Research Developing and applying deep learning models for predicting protein structures and molecular interactionsExploring model architectures and training approaches, including geometric deep learning, diffusion models, and graph neural networksPreparing and processing biological and structural datasets for model training and evaluationImplementing and maintaining research code and computational workflowsTraining, fine-tuning, and evaluating models using GPU and high-performance computing resourcesDesigning computational experiments to assess model accuracy, efficiency, and generalizationComparing model performance with existing methods using appropriate benchmarks and evaluation metricsInvestigating model limitations and exploring ways to incorporate physical and biological informationReviewing relevant scientific literature and adapting promising methods to ongoing researchResearch documentation and communicationDocumenting methods and experiments to support reproducibilityResearch coordination and collaborationPreparing figures, reports, presentations, and contributions to scientific publicationsCollaborating with researchers in computational biology, biochemistry, and biophysics to guide model development and interpret results