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Field
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tools and statistical methods. Develops and optimizes imaging protocols, algorithms, or AI/ML models for diagnostic or research applications. Assists with preparation of research manuscripts, abstracts
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learning, physical AI, and spatial AI. The RF will contribute to the development of innovative algorithms, data preparation pipelines, and experimental evaluations that are central to the Physical
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research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research
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engine modelling and simulation, intelligent control strategy development, and data-driven calibration, applying AI and optimisation techniques, including machine learning and metaheuristic algorithms, to
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include: conduct scientific research in the field of machine learning. develop novel machine learning algorithms primarily for representation learning, dimensionality reduction, clustering and search
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with cryptographic implementation, demonstrated through previous projects on software and/or hardware implementation of cryptographic algorithms (not restricted to symmetric-key algorithms). For
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communication signals themselves can simultaneously provide reliable wireless connectivity and high-precision sensing. The objective is to develop localisation and sensing algorithms that achieve near
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integration and advanced signal processing algorithms for structural and functional imaging, in order to deliver clinically relevant prototypes, high-quality publications, and translational outcomes for the
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applications. In this position, your primary task will be to lead the development of algorithms, software, and hardware to extend the current HAUCS framework. This includes developing the sensors
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of computational pediatric cancer research. They will develop and support data processing and analysis tools, create new algorithms for focused research questions in bulk and single cell tumor