Digital Transformation Internship
Description of the job
The sponsoring organisation is Airbus in UK and the opportunity is at the Bristol Filton site.
At Filton, over 4,000 people are employed in the design and manufacturing areas, as well as in business support roles such as procurement, finance and customer service. More than 2,000 engineers at Filton are involved in a range of areas, including wing integration, flight physics, structures and systems - with several hundred engineers also working on research and technology for future aircraft projects.
The internship is proposed by the 'Digital Wing' team within the Engineering Wing Structures function. They are responsible for developing process methods and tools for the Engineering function, as well as industrializing new technologies such as data analytics and immersive technologies.
The intern has the opportunity to be involved in a variety of work during the internship. Most of the work is split into two disciplines:
- Big Data Analyses - This is an opportunity will build on the intern's skills in data-driven analytics and coding. With massive amounts of data available from test, simulation (including virtual test), industrial and operational sources we require efficient ways of identify value in the data.
- Windows Holographic Development - Airbus is a strategic partner in the Microsoft Hololens programme, giving us early access to holographic hardware and software.
Big Data Analyses:
- The objectives below ramp up in complexity and are in order of priority. It could be possible for a particular student to choose an objective where they have the most interest and experience and then focus on that objective.
- Detect trends/anomalies from set of KPIs obtained by processing raw data Removal of known system and environmental natural influences/behaviours, such as load cases and temperature, enabling the identification of unexpected trends that in turn highlight unforeseen system influences/behaviours. Model data may be used as a reference of the expected behaviour.
- Detect trends/anomalies directly from raw time series data using a learning approach Application and tuning of a learning system to identify "events" directly within time series data and then recognise the signature of these events. Identification of the true physical relationships. Application of this technique to detect trends/abnormalities.
- The evolution of the correlation matrix over time Ability for sampled data to efficiently calculate a correlation matrix for a time frame and then analyse the evolution of this over time.
Must be an undergraduate for the full duration of the placement studying towards a degree in one of the following (or similar):
- Data Analytics
- Data Science
- Software Engineering
- Computer Science
The student requires a pro-active, confident personality who is able to engage customers and key stakeholders. They will be supported but the business however stakeholders will be busy with operational demands. It requires confidence to approach contacts and secure their time to deliver the required solution. Moreover, this is a real Engineering environment and the data/tools may have gaps/bugs/issues that need to be addressed or worked around. Hence, patience, flexibility and adaptability are important qualities.
General programming experience, particularly in the scope of one/both of the following:
- Data Science and Analytics. Languages we prefer: Python, Spark, R. Matlab and Simulink are regularly used by our Engineers and may prove useful. It can also be useful to be familiar with visualisation tools such as Spotfire.
- Game Engine Development - Unity development, C#, .net framework, UWP. Game modelling and design may prove useful
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