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, energy, geoscience). Explore the combination of Generative AI, logic-based models, and systems engineering principles to support next-generation digital workflows — including methods for knowledge graph
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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regarding research results. Preferred Qualifications: Experience with deep/graph neural networks and active involvement in data science and machine learning projects. Experience in multimodal data fusion (e.g
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representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture optimisation of foundation models Inverse design of next
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations
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available for two years. Keywords: Geometric Deep Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial
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Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation Commitment to Diversity The University
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Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation. Commitment to Diversity The University
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background in systems thinking, analysis and modelling Experience in teaching and supervision in higher education at least on MSc level Knowledge of graph theoretical approaches and graph signal processing