Revolutionising remote working: success stories in Quantum, Space and AI
An exciting networking opportunity to hear from experts from Space, AI and Quantum about their remote working stories and the knowledge transfer opportunities.
The global pandemic and the new working restrictions have transformed the working routine for many universities and businesses across several sectors. And while the remote working journey has been different for each one of us, some experiences are particularly successful and revolutionary.
Webinar recording is now available
Join us for the most exciting networking opportunity to hear from experts from Space, AI and Quantum about their remote working stories and the knowledge transfer opportunities.
- Dr Nicol Caplin, European Space Agency
- Scott Sleegers, University of Sussex
- Dr. Teodoro Laino, IBM Research Europe
Remote work in outer Space- Developing Experiments for the ISS by Dr. Nicol Caplin, European Space Agency
The International Space Station is the largest habitable satellite which acts as a factory, observatory and laboratory. To use the ISS as a laboratory, the experiments conducted need to be designed very carefully as they need to survive not only the launch but also the re-entry into the atmosphere and the recovery. Furthermore, the experiments have to be designed in such a way that they can be easily conducted by one astronaut, who may not necessarily be familiar with the topic under investigation. This requires teams of engineers and payload specialists that take the lead on making the experiment function within the limits of mass, size and power consumption.
During this talk, Dr. Nicol Caplin will give us a brief overview of how these processes work, what needs to be considered and what protocols are in place when launching experiments into space.
Remote Monitoring and Control of Ultracold Atoms Research by Scott Sleegers, University of Sussex
Bose-Einstein Condensates (BECs) are known as the fifth state of matter. When an atom cloud drops below a critical temperature, around 1 µK, condensation is achieved, and the atoms behave as a single quantum object. This cloud is now very sensitive to magnetic fields, allowing them to probe the magnetic field across the surface of a sample, with high sensitivity and spatial resolution, and can be thought of as a magnetic microscope.
Their experimental set-up allowed them to produce a BEC in a lab that did not have a BEC before while working with restricted access to the lab. The required precise and time-sensitive control of the experiment’s numerous laser beams and power supplies is achieved using purpose-built open source hardware and software. Microprocessor-based electronics are also used to log environmental parameters, such as laser power, temperature, and pressure. These data, and experimental measurements, are stored in a database which allows convenient access through any web browser meaning they could debug problems remotely.
Scott Sleegers will discuss what creating a BEC entails, talk about the intricacies of our set-up, and showcase the flexibility it provides them.
Dr. Teodoro Laino, IBM Research Europe
Artificial Intelligence (AI) has emerged as a valuable complement to human knowledge and creativity in organic chemistry – for tasks like predicting chemical reactions, retrosynthetic routes or for digitizing chemical literature. Here, they present the first implementation of a cloud-based AI-driven autonomous laboratory.
The remote laboratory is made accessible to chemists through the cloud and is equipped with automation technologies. The AI assists remote chemists with several tasks: designing retrosynthetic trees and suggesting the correct sequence of operational actions (reaction conditions and procedures), or ingesting literature on synthetic procedures to convert them into an executable program. Following supervision by synthetic chemists, the AI self-programs the automation layer and makes decisions on the synthesis execution using feedback loops from analytical chemistry instruments.
Dr. Teodoro Laino will present the platform architecture and its performance across various classes of synthetic tasks.