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Field
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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, or similar. Solid expertise in computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the
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vision, machine learning, computational imaging, surgical data science, biomedical engineering, medical image computing or a closely related discipline. They should have strong knowledge of AI, computer
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are particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine learning
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. For NeuroAI focus: Analyze high-dimensional brain data, document results and carry out an independent research project. For NeuroAI focus: Develop and apply new machine learning and computer vision methods
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demonstrate how innovative methodologies can generate new knowledge within art history. Relevant topics may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums
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particular emphasis on integrating satellite LiDAR and UAV data with field observations. Applying statistical modelling, automated machine learning approaches, and artificial intelligence for the analysis and
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assigned by Supervisor. Requirements PhD in Computer Science or related field Expertise in computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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has a fixed-term opportunity for a Postdoctoral Fellow / Senior Research Fellow to contribute to world-leading research in continual learning, computer vision, multimodal foundation models, and