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programmes. This is an exciting opportunity to teach within a modern curriculum that combines computer science, artificial intelligence, software engineering, cloud computing, machine learning and responsible
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, Django, FastAPI, or Streamlit) Experience deploying applications to cloud platforms (e.g., AWS, Azure, or GCP) Experience with API integration and working with external data sources Desirable Criteria: PhD
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researcher who holds a relevant PhD/DPhil with post-qualification research experience and a strong lead-author publication record. You will have excellent Python and research software engineering skills
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to support your professional growth throughout the project. What we are looking for: You should be educated to PhD level in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science
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of the molecular ISM of galaxies, with a focus on galaxy centres. In particular, this project aims to measure the spatially resolved properties of giant molecular clouds and/or weigh the supermassive black holes
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CUDA kernels and/or Triton, and inference engines (vLLM, SGLang, et cetera). Experience coding with deep learning libraries such as Pytorch/JAX is essential. Applicants must hold a PhD in computer
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software packages . We develop and work with state-of-the-art, high performance computing and cloud infrastructure to realise collaborators’ data science and artificial intelligence research at scale. HOW
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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are looking for: Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. operating systems, networking, distributed systems, cloud computing. Research Assistant: A
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to build ‘design-build-test-learn’ pipeline through computational design and experimental validation. About you PhD (or in the final stages of PhD submission) in computational biology, evolutionary genomics