Thesis on the topic "Examining the Limits of Predicting Human Mobility Behavior" from November 2024

We in the "Development Cloud enabled Charging" department are responsible for the development of innovative charging services for electric vehicles in the Mercedes-Benz vehicle backend, this includes the customer-oriented development of the user experience (UX), the implementation of the charging services in the cloud and the rollout in the markets worldwide.

We are looking for you as a "working student" for this challenging and exciting area of responsibility.

With the increasing popularity of electric vehicles (EV), the storage capacity of their batteries can help to support the electrical grid, which is becoming unstable due to increased renewable energy generation. However, in order to seamlessly integrate this capability without restricting user mobility, planning the mobility patterns of individual users becomes relevant. Nevertheless, not all users adhere to uniform mobility habits and therefore the performance of predictive algorithms may vary for specific users or user groups.
The efficient development and operation of smart charging services using predictive algorithms requires quantifying the predictability of users, in particular answering the research questions:

• How accurately can human mobility behavior be predicted?
• Which user attributes can predict a user's predictability?

In an attempt to answer these research questions, the following tasks were to be carried out over a period of six months:

• Research the literature to determine the state of the art relevant to predicting human mobility behavior and its constraints
• Descriptive analysis of real mobility data from EV drivers
• Definition of suitable metrics to quantify the predictability of the user's mobility behavior
• Implementation and evaluation of standard algorithms for predicting mobility behavior in relation to the defined metrics
• Documentation of all steps and findings in a written manuscript

The final topic will be determined in consultation with the university, you and us.

• Degree in mechanical engineering, computer science or comparable training
• Confident written and spoken German and English skills
• Confident handling of MS Office
• Commitment and ability to work in a team
• Analytical mindset and strategic way of working

Additional information:

Of course, we can't do without formalities. Please apply online only and attach a CV, current certificate of enrollment stating the semester of study, current transcript of records, relevant certificates (max. total size of attachments 5 MB) and mark your application documents as "relevant for this application" in the online form.

Further information on the recruitment criteria can be found"here".

Nationals from countries outside the European Economic Area should send their residence/work permit with their application.

We particularly welcome online applications from severely disabled persons and persons with equivalent disabilities. If you have any questions, you can also contact the site's representative for severely disabled employees at SBV-Sindelfingen@mercedes-benz.com, who will be happy to support you in the further application process after your application.

Please understand that we no longer accept paper applications and that there is no entitlement to return postage.

If you have any questions about the application process, please contact HR Services by e-mail at myhrservice@mercedes-benz.com or by phone: 0711/17-99000 (Monday to Friday between 10 a.m. - 12 p.m. and 1 p.m. - 3 p.m.).