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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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preferably) Strong skills in turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and
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mechanisms against experimental databases on combustion and emission characteristics. Develop and apply Computational Fluid Dynamics (CFD) models to investigate intermediate reaction pathways and pollutant
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capacity around Water and Sanitation in low- and middle-income settings across Southeast Asia. This position offers a unique opportunity to contribute to field-based public health research and program
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Application Deadline 10 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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. This also includes using AI-based modelling, learning-enhanced co-simulation, and predictive analytics to improve the fidelity, automation, and scalability of quantum communication security evaluations. Key
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for funding agencies and stakeholders. Job Requirements: A PhD degree in Mathematics, Geophysics, Physics, Computer Sciences, or related areas. Strong background in numerical modeling and high-performance
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prediction models in Neurology using EEG data via Deep Learning (DL) techniques. In this prospective and longitudinal study, the outcome of interest is cognition over time. This position will be under
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised