INTERN - Data Engineering
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Candidate profile: Required – Statistics focused academic background. Desirable – Sequence modelling experience (Markov Chains, RNNs, LSTM, Sequence-to-sequence, Transformer networks), and Reinforcement Learning.
Project: There are four candidate projects for a ten-week internship in the Process Understanding pillar. The intern’s prior experiences will determine which one will be most appropriate. For illustration only one is described below:
Determine how Transformer Networks can be applied to the LPM discovery problem and measure how they perform compared to the auto regressive HMM approach
Week:
- Introduction and literature review.
- Literature review.
- Analyze data and see how UiPath has adapted AR HMMs to the problem.
- Adapt Transformer Network Implementations to fit the input data.
- Model optimization.
- Analyze and summarize results.
- Peer review.
- Make changes to the implementation and continue to experiment.
- Final presentation of results to all stakeholders.
- Handover, feedback sessions and social.
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