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Field
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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. Knowledge of data-driven analytics, machine learning, signal processing, or advanced modelling techniques relevant to power systems. Experience with real-time simulation platforms, hardware-in-the-loop
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, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno-economic study, design and analysis of integrated systems. Experience with energy system
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or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the
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optimisation; uncertainty and sensitivity analysis; and machine learning or AI-supported optimisation. Strong analytical and programming skills are essential. Relevant experience may include tools and languages
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Requirements: Candidates should hold a PhD in civil engineering, computer science, electrical/computer engineering, robotics, or a related field. Experience in computer vision, deep learning, 3D reconstruction
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, such as computer science, neuroscience, engineering, physics, applied mathematics (or be near to completion of their PhD). Skills in computer programming (especially Python or C++ or Matlab) and
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, such as computer science, neuroscience, engineering, physics, applied mathematics (or be near to completion of their PhD). Skills in computer programming (especially Python or C++ or Matlab) and
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning models, high