A I / VR / AR - Health
How can AI technology be used to resolve issues on the robotic system in a more automated way to improve data outcome?
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Pivot Park Screening Centre case
Pivot Park Screening Centre uses High Throughput Screening (HTS) to find starting points for new medicines. This process is performed by robots in a fully automated manner.
Scientists identify issues in the resulting data by recognizing patterns in the results, but this is not always efficient. It is proposed to investigate how AI technology can be used to resolve issues on the robotic system in an automated manner. Can we detect automatically that a suspicious pattern occurs? Can the AI system re-instruct the robot to avoid the problem (e.g. use another instrument)? And more broadly: Can AI be used in other, more sophisticated ways to improve quality and efficiency of the robot system?
Recommended background and tools
In order to take on this challenge it is recommended to have knowledge about lab automation, data mining and AI. Knowledge of high throughput screening would be beneficial. The expected answer should be on PhD level.
Software used is HighRES Biosolutions’s Cellario (robot scheduling), IDBS’s ActivityBase (dataprocessing and authorization), Dotmatic’s Vortex (data mining) and KNIME (pipelining). Experience in those software packages or similar software would be helpful.
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