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Field
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employment decision is made. Doctoral degree in remote sensing, geoinformatics, computer science, or a closely related field. You have experience developing and applying deep learning models for Earth
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languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer architectures, attention mechanisms, and fine tuning techniques
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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profile PhD in Computer Science, Data Science, Machine Learning, or a related discipline. Proven experience in computer vision (e.g. image processing, deep learning, object detection, segmentation) and
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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-genome deep-sequencing data collected as part of the Office for National Statistics Covid Infection Survey, with a focus on using household data to enable methods for determining who-infected-whom using
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values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Deep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and
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learning on graphs, including knowledge graphs, social and biological networks, financial networks, temporal and dynamic graphs, uncertain graphs, explainability and fairness of deep learning methods
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design