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Field
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related field Sound knowledge in the field of artificial intelligence and machine learning Ideally experience with knowledge graphs, semantic search, graph neural networks (GNNs), explainability
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packages, computer coding, and statistics is highly desired. Past research assistants have been accepted to strong PhD programs in clinical and experimental psychology, epidemiology, and medical school
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be expected to seek external funding. The Department has a strong PhD program in Statistics, Graph Theory, Combinatorics, and Applied Mathematics, with very active research collaborations with other
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high throughput cellular assays. Perform statistical analysis and proficiently use graphing software or R scripts. Successful Candidates Will Have: Experience in analysis of protein kinases
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of the College of Engineering in the development and preparation of research. This will entail working with teams of PhD-level researchers and staff to conceptualize and draft research proposals. This position
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and use machine learning methods (mainly graph neural network architectures) to design representations and transferable energy models for proteins and materials. The position will serve to develop your
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Participation in publications and their presentation at conferences Participation in teaching and independent teaching of courses as defined by the collective agreement Supervision of students and PhD students
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& Informatics, to work on the InteGraL project (“Interpretable Graph-Based Machine Learning”). This Leverhulme Trust funded project is focused on developing alternatives to Graph Neural Networks (GNNs). Its
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meet, the following criteria: Have a PhD in Computer Science, Computer Engineering, Electronic Engineering (or related discipline). Demonstrate a strong record (commensurate with career stage
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and efficient operation of projects Coordinate with stakeholders to support the smooth execution of the projects Perform data collection and entry, analyses and reporting, including developing graphs