Job Description

A leading premium automotive business and leading to move towards electric autonomous vehicles.

WHAT TO EXPECT

A talented Engineer is sought in support of creating artefacts for vehicle efficiency control functions which are model based. This is an opportunity to use physics based high fidelity models and create functional blocks of model based controller and energy optimisers primarily.

This is an exciting space to be part of as you have access to data lakes and get to use surrogate modelling, data modelling, artificial neural network & model order reduction for creating the functional blocks to be implemented into control system for thermal & electrical efficiency of JLR vehicles. You get to combine domain knowledge of thermal and electric energy management with data and artificial intelligence based advanced controls.

Key Accountabilities and Responsibilities

• Interrogate physics-based energy & thermos-fluid models and create functional model blocks for controller implementation by control systems team.
• Creating reduced order models for real time controller implementation.
• Create surrogate models from 3D & 1D CFD models output, chose right training data set and data training protocol; They can range from polynomial response surfaces to Bayesian networks or Gradient enhanced kriging (GEK) or Artificial Neural Network (ANN).
• Create virtual sensors where necessary for solving thermal & electrical energy management control optimisation problems.
• You get to collaborate with the Control System Domain teams for implementation of the function blocks.
• Support the automation of data pipelines feed into the optimisers and models for thermal and electrical energy management.

WHAT YOU\'LL NEED

We are keen to secure someone with experience or theoretical knowledge of the following (perhaps as a Masters, PhD graduate or Research Associate) that has a background in Physics, Mathematics, Statistics or Computer Science along with exposure to thermal, electrical and energy management principles & theories.

You will have expeirence or knowledge of the folloiwng too:

• Bayesian modelling and/ or Machine Learning background.
• Experience with surrogate modelling.
• Proficient in Matlab, Matlab Simulink, Matlab Deep Learning toolbox & Python
• Experience with Machine Learning Libraries e.g., PyTorch/ Keras/ Tensorflow
• Model Order Reduction - concept level understanding

JLR