I think the strongest impression that was left with me was that I really enjoyed the start-up culture. The responsibility I was immediately given was empowering as I felt I could take ownership of my project, and the fact that the company is not weighed down with complicated internal administration meant I could get my code into production and see its effect on the business very quickly. The exposure to the whole business and the ability to have an impact on the company's future in such a short space of time, has seriously impressed me and will be something I consider when applying for work in the future. Despite only meeting a couple of members of the team face to face, I could join them on weekly company-wide virtual calls which I look back on warmly. Everyone gave the impression of being dedicated to growing the company and the battery sector but could step back and have a good time whilst doing it too. What advice would you give to future interns? If there are areas or members of the business you would like to speak to just ask your mentor if they can set you up a meeting. Working remotely did not stop the ability to meet the whole company if you want to. Setting up a regular catch up session with your team or supervisor was very useful in structuring my daily work and project. Zenobe already had an excellent plan for this but if there is not one in place when you start, I would really recommend trying to keep in regular contact with your team.
Cormac Sarch Thomas, Wadham College, Final Year Undergraduate, MEng Engineering Science, Remote Working Work Projects For my internship at Zenobe I was working in the Data Science Team, which consisted of 5 people (including me and another intern). I worked on two main projects during my time at Zenobe. The first of these was developing a python model of a battery providing frequency response services to the national grid. This model took a week or two to complete. With the completed model I explored how to optimise the operation of the batteries while providing frequency response services. This involved forming a cost function and performing sensitivity analyses over different values for the operating parameters of interest. The final optimal scenario was able to increase income significantly.
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