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Field
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the following mandatory requirements: a) A completed degree in Chemical and Biological Engineering; b) Good knowledge in the areas of Machine Learning, Microbiology, Knowledge Graphs, and Language
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receipts of proposals, and maintaining a system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results
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-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound, MICCAI, 2023 [3]Trosten et al., Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few
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-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound, MICCAI, 2023 [3]Trosten et al., Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few
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various sets of data to prepare databases for cross-disciplinary AI research and learning. Incorporates open knowledge graph networks. Takes the lead in drafting scientific papers and technical documents
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. Investigate and build robust data and AI agent pipelines for continuous learning and knowledge acquisition, including annotation strategies and knowledge graph development for aquaculture stress events. Design
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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, consolidating metadata and other various sets of data to prepare databases for cross-disciplinary AI research and learning. Incorporates open knowledge graph networks. Takes the lead in drafting scientific papers
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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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degree in mathematics, strongly encouraged to apply. Experience and demonstratable knowledge in deep learning and one or more of the following: transformer networks, implicit neural functions, graph neural