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: Steinberg Centre for Simulation and Interactive Learning (SCSIL) The Surgical Performance Enhancement and Robotics (SuPER) Centre in collaboration with Steinberg Centre for Simulation and
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high-performance computing ML inference or serving systems (vLLM, SGLang, PyTorch internals, or similar) Interactive theorem proving (Rocq, Lean, HOL, Isabelle, or similar) or other formal methods
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GPU computing, distributed training, high-performance computing, or cloud computing. Experience with probabilistic forecasting, ensemble methods, uncertainty quantification, data assimilation, or
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learning, and predictive modeling. Experience contributing to peer-reviewed publications. Familiarity with cloud computing, Linux environments, and high-performance computing platforms. Additional
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-performance computing (HPC) environments and/or cloud-based data processing infrastructures Familiarity with workflow management systems (e. g. Nextflow) and workflow orchestration Experience with large
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, bioavailability, and imaging performance. Develop high throughput spectroscopic assays, and single molecule methods to evaluate key properties, including spectral profiles, photostability , and
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and high-performance computing to build and run advanced numerical models, carry out simulations and analyse results in a systematic and transparent way. Depending on ongoing projects, you may be
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multi-omics data and the genetic architecture of complex traits. The PHRC offers a multidisciplinary environment with access to genomic, clinical and lifestyle phenotypes, and high-performance
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contribute to the design and implementation of the self-supervised learning tasks used to pretrain models on the resulting data, and support training runs on high-performance computing resources
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(e.g. GitHub/GitLab), version control, and maintaining well-documented, reusable repositories. Working experience with high-performance computing environments. A strong interest in skeletal muscle