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the phenomena and scales at play. The next step will be to program this model into the FullSWOF software and to carry out numerical simulations, followed by comparisons with field measurements. He or she will
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discipline. Strong experience in numerical/computational modelling (e.g., FEM/multiphysics, wave propagation, computational mechanics). Evidence of scientific programming and good software/reproducibility
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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high-resolution urban climate modelling within a nationally coordinated research program involving NEA, A*STAR, NTU and NUS and international collaborators. Qualifications Applicants must have a PhD
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, seeks to recruit a junior research scientist to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based
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algorithm development and system performance. Technical Focus Areas Computer Vision and Model Development: Design and train deep learning models for insect classification and morphological recognition
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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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) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
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experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Design of a simulation model for the cutting of metallic and non