RESEARCH FIELDComputer science › InformaticsMathematics
RESEARCHER PROFILEFirst Stage Researcher (R1)Recognised Researcher (R2)Established Researcher (R3)Leading Researcher (R4)
APPLICATION DEADLINE31/10/2019 00:00 - Europe/Athens
LOCATIONFrance › Toulouse
TYPE OF CONTRACTTemporary
OFFER STARTING DATE12/11/2019
Scientific challenge description
Automobile manufacturers are confronted with major technological challenges regarding the development of new drive trains.
On one hand, strict legislation and regulation requiring overall and continuous reduction of fuel consumption and emission levels of new vehicles, as well as the emergence of new energy types: electric, hybrid, fuel cells.
On the other hand, rising customer demands regarding vehicle dynamics, overall comfort and affordability.
Continental Automotive focuses on the development of new powertrain technologies to fulfill the above demands. Continental believes that machine learning techniques can play a substantial role in the development of future drive trains, as manufacturers are increasingly reaching limits with traditional engineering methods.
We are willing to leverage machine learning to obtain reliable, interpretable simulation results, which can be used to predict drive train components behavior in extreme situations.
Also, we want to understand how to optimize the type and amount of data to be collected so as to optimize collection costs.
2. State of the art current limitations
Current physical models meet three major challenges: development complexity, output quality, execution time. We hope to overcome some or all of these challenges with data based models.
Data based models also come with their limitations: there is a trade off between interpretability and quality. Also, outcome confidence level is hard to estimate, especially when predicting outcome outside of the range of observed training data.
Scientific research themes
To be confirmed with academic partner
From a theoretical standpoint, we expect the candidate to develop training and validation machine learning methods that solve the challenges mentioned above.
From a practical standpoint, we would like to apply these methods to one or more concrete cases and integrate them seamlessly in the simulation development chain so as to improve its quality and execution speed.
Industrial applications examples
Calibration of electric engine
Polluants prediction for internal combustion engines (NOx, CO, particles)
Required data for the challenge
For some of the challenges, data is readily available.
For others, data is partly or not available yet and collected strategies will have to be defined and executed.
Funding category: Cifre
PHD title: PhD in Machine Learning
PHD Country: France
We are looking for a curious mind with machine learning skills, excited to push the boundaries of simulation with AI in order to enable tomorrow's mobility: safer, cleaner, connected and for everyone
EURAXESS offer ID: 446624
Posting organisation offer ID: 87437
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