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Field
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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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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a strong statistical and computing background. The appointee should also have a high level of
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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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, deep learning, or statistical modeling. Demonstrated experience working with clinical, digital health, or related biomedical data. Proficiency in Python, R, or other scientific programming languages
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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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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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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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well as the underlying signaling network for a variety of diseases including aging, cardiovascular diseases, brain diseases, cancer, et al., through novel statistical or deep learning methods development and application
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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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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with