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methods and research software empower modern biomedical research. Your mission As a Postdoc you will help build the next generation of multimodal and explainable AI models for personalized risk prediction
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of antibiotic resistance. You will build generative protein models to predict plausible future resistance mutations, use these models to guide high-throughput experimental screens of millions of enzyme variants
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interdisciplinary team working at the forefront of AI-driven biomedical research. Your work will contribute directly to improving our understanding, prediction, and treatment of disease. Your main tasks include
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of artificial intelligence, multi-omics data integration, and functional genomics, aimed at predicting synthetic lethality in cancer - including representation learning, nonlinear embeddings, and predictive
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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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sequences. You will develop advanced modeling techniques to create privacy preserving realistic data, and predict disease trajectories, outcomes, and other clinically relevant endpoints. Moreover, you will
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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prediction of gene perturbation effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers