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Field
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transportation contexts, and machine learning classifiers Heuristics & Solvers: Develop and refine custom heuristics and metaheuristics (e.g., Tabu Search, Genetic Algorithms) to find high-quality solutions
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methodologies or models from an engineering perspective, as well as scientific studies focused on understanding deep learning. This includes the development of novel applications of artificial intelligence
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of AI and machine-learning Develop and execute methods for characterizing and linking the texture and structure of alternative protein foods Design, execute, and interpret experiments independently
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for cell transplantation therapies in animal models of Alzheimer's disease, stroke, and epilepsy. To achieve this goal, the candidate will combine a gene network-based approach with a machine learning model
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focused on precision lung cancer prevention and early detection, with an emphasis on risk prediction modelling using artificial intelligence and machine learning. Major duties and responsibilities include
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Machine Learning, Deep Learning, Computer Vision, Large Language Models, Data Analytics, or Intelligent Systems will be advantageous. Key Competencies Prior experience in research project coordination and
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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Understanding of & ability to contribute to broader management/administration processes Experience developing & applying machine learning models to computer vision tasks Practical experience in computing
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, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow