Successful TTS staff are known for effectively applied expertise, continuous learning, thoughtful attention to detail, timely and appropriately calibrated follow-up, proactive and constructive problem solving, openness to new approaches, and for building effective partnerships and trust within TTS, across the university, and (where relevant) with external partners. Strong experience installing, configuring, maintaining, troubleshooting common frameworks and software used in research and high-performance computing such as scikit-learn, TensorFlow/TensorBoard, Keras, Theano, Caffe, Pytorch, MXNet, DGL, GPU libraries such as NVIDIA RAPIDS suite (cuDF, cuML, cuGraph, cuDNN).