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Field
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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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: Expertise in machine learning, deep learning, natural language processing and other AI methods in health and life sciences datasets Expertise in advanced computational methods such as network analysis, graph
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at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation
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-relational technologies such as SQL/PLSQL, MongoDB and graph databases (e.g., Neo4J). Proven experience in design and development of networked applications and distributed systems. Knowledge of computer
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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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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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scholars/fellows on the status of research. Collect and log laboratory results, clinical outcomes and/or survey data. Evaluate and perform data analysis using graphs, charts or tables to highlight the key
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to work in an interdisciplinary environment. Desirable Skills: Experience working with or supporting a scientific facility/instrument platform. Knowledge of graph-based methods, manifold learning
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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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language processing and other AI methods in health and life sciences datasets Expertise in advanced computational methods such as network analysis, graph databases and structured and unstructured data mining tools