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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers
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Information , Data Visualization , Deep Learning , High dimensional Data , Large Language Models , Large Scale Optimization , Machine Learning , Natural Sciences , Public Interest Tech Computational Biology
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, interpretation of modelling results, and publication-driven research Ability to work independently, efficiently, and with clear scientific judgment Fluent oral and written communication skills in English Desired
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models and other natural language processing techniques applied to language understanding and the next generation of search engines. AI + Health: This area levarages AI to study and solve complex problems
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academic model, with the ambition of positioning Africa-driven research at the global forefront and establishing itself as a university of international standing. Beyond the traditional role of a university
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driven inverse design of functional materials. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data
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focus on the conceptualisation and prototyping of spatial data infrastructures and registries for space environments and objects. The role involves research into spatial data models for representing
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Research Fellow - Data Scientist/Clinical Data Engineer Job No.: 690434 Location: 553 St Kilda Road Employment Type: Part-time, fraction (0.5) Duration: 12 month fixed-term appointment Remuneration
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this cross-functional role, the postdoctoral fellow will develop AI-driven methodologies to bridge the gap between genomic evidence and safety outcomes, addressing a critical challenge in pharmaceutical