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Field
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independently and as part of a team Preferred Qualifications Experience in graph-based AI models, multi-omics data integration, or network inference Background in epigenomics, gene regulation, or aging biology
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limited to artificial intelligence, computing theory (algorithms, complexity), data science, statistics, discrete mathematics (graph theory, combinatorics), game theory, machine learning, optimization
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: extremal graph theory, Ramsey theory, probabilistic combinatorics. • Candidates should have (or be near completion of) a PhD in mathematics. • Candidates should have a strong research record in Combinatorics
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on processing and developing representation models for diverse data sources, including time-series data (EEG, video, mass spectrometry) and chemical data (molecular graphs, SMILES strings) related to odorant
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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interoperable methodological framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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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 collected in accordance with the research