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Research and develop autonomous decision-making algorithms for process industries (e.g., chemical, pharmaceutical, materials, energy) or discrete manufacturing (e.g., electronics assembly, automotive, home
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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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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for scholars who have obtained their doctorate more than 5 years prior to the start of the fellowship (1 September 2027). The Fellowship programme is open to post-docs, tenure-track academics and those wishing