Vehicle Dynamics Data & Performance Placement (Student)

Red Bull Technology Group; Milton Keynes; On-site; GBP 18,000 - 24,000; Full time

Benefits offered by this job

daily food allowance

free gym access and fitness classes

multiple discounts and offers with our

partners

free coffee

unlimited Red Bull

Job summary

Red Bull Technology Group invites applications for Industrial Student Placement positions within the Vehicle Performance Group, with two openings in Suspension and Simulation Performance teams. Ideal for Mechanical, Aeronautical or Automotive Engineering students, or STEM disciplines showing strong interest in vehicle performance and data analysis.

Responsibilities include predictive performance analysis, model validation with track data, and real-time/ad-hoc data analysis during race weekends,

Qualifications

• Excellent mathematical and analytical skills.
• In-depth knowledge of vehicle dynamics principles.
• Experience with programming/data-science, Python would be advantageous.
• Good written and face-to-face communication.

Responsibilities

• Providing predictive performance analysis prior to each event.
• Validating models against track data and developing them to improve accuracy.
• Supporting real-time and ad-hoc data analysis of race and test events.
• Developing automated routines and processes for data analysis and metrics generation.
• Utilising design-of-experiments methods and existing toolsets to explore the vehicle design space.
• Improving the visualisation of data so that the wider company can quickly understand conclusions.

Skills

Mathematics

Vehicle dynamics

Communication

Tools

Python

Job description

Red Bull Technology Group invites applications for Industrial Student Placement positions within the Vehicle Performance Group, with two openings in Suspension and Simulation Performance teams. Ideal for Mechanical, Aeronautical or Automotive Engineering students, or STEM disciplines showing strong interest in vehicle performance and data analysis.

Responsibilities include predictive performance analysis, model validation with track data, and real-time/ad-hoc data analysis during race weekends,