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processing, machine learning, statistics or related fields. Demonstrated expertise in ML/AI, with prior experience of applications in the healthcare domain, particularly in cancer research considered a strong
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Degree Doctor of Philosophy (PhD) / Doctor rerum naturalium (Dr rer nat) Course location Hannover In cooperation with Twincore - Centre for Experimental and Clinical Infection Research, University
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includes, but is not limited to: photonic data processing and communication systems; AI-driven optical signal processing and network optimisation; machine learning for photonic systems modelling, control
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. Recognised Researcher position has been opened. The ideal candidate holds a master's-level background in robotics, AI or related fields, with strong Python/C++ skills and experience in machine learning
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applications. The project integrates: Computational Fluid Dynamics (CFD) and multiphase flow modeling Radiative heat transfer Machine learning and reduced-order modeling Data-driven optimization for industrial
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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theoretical approaches to machine learning and artificial intelligence. Specific Requirements Having the status of a doctoral student; Strong mathematical skills and knowledge; Experience in math/coding
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets