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Predoctoral Position (R1): AI applied to the intelligent validation of distributed automotive architectures through unsupervised learning and environment simulation

Centre de Visio per Computador -
Bellaterra, Barcelona provincia

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Detalles del empleo

Cualificaciones

Doctorado
Visión artificial
Inglés
Máster
Grado
Machine learning
Test cases
Inteligencia artificial
Liderazgo
Python

Descripción completa del empleo

Predoctoral Position (R1): AI applied to the intelligent validation of distributed automotive architectures through unsupervised learning and environment simulation

Closing date: Until position is filled

DESCRIPTION OF THE RESEARCH PROJECT

CONTEXT: The increasing complexity of modern vehicle E/E architectures is surpassing the capabilities of traditional validation processes. Manual test-case creation has become slow, error-prone, and insufficient to cover real operating conditions or rare edge cases. Moreover, these conventional methods rely heavily on physical prototypes, resulting in higher development and logistics costs and longer development cycles.

DESCRIPTION: This project proposes the development of an AI-driven validation system capable of learning in an unsupervised manner from internal communication data collected from series-production vehicles. Based on this data-driven learning, the system will be able to detect anomalies and unexpected behaviors in development vehicles without requiring predefined test cases. Additionally, its integration with simulation environments will enable the generation of realistic and data-driven test scenarios, significantly expanding validation capabilities.

BENEFITS: The expected outcomes include a reduction in the number of physical prototypes required, leading to substantial cost savings; increased validation coverage by allowing AI to detect edge cases that typically go unnoticed in manual testing; and a shorter time-to-market thanks to the automation and intelligence introduced into the validation process, ultimately accelerating vehicle development and release timelines.

CANDIDATE PROFILE

Candidates should have solid training in AI and physics/mathematics, and knowledge in quantum physics, acquired through bachelor's and master's degree studies. For example: Physical Degree + Master's Degree in AI; AI/Computing Degree + Master's Degree in Quantum Computing; etc

Applicants are expected to be fluent in both oral and written communication in English.

Professional experience of interest: Experience in machine learning model development and data analysis, as well as Python domain and AI tools, will be valued. It is also positive to have notions of quantum computing and to have participated in research projects or academic works related to artificial intelligence. The experience in data or automotive systems will be a plus, but it is not mandatory.

Other requirements: Solid foundation in mathematics (linear algebra, statistics, and optimization) will be valued, as well as the ability to work with large volumes of data and interest in simulation environments. Autonomy, critical thinking, motivation for research and a proficient level of English are also important. Additional merits will be considered: participation in scientific projects, contributions to software or activities related to artificial intelligence, quantum computing, or technological innovation.

HOSTING INSTITUTIONS:

Computer Vision Center (CVC), www.cvc.uab.cat

SEAT, www.seat.es

This position will be developed within the framework of the Industrial Doctorate program of the Generalitat of Catalonia.

CHARACTERISTICS OF THE INDUSTRIAL DOCTORATE PROJECTS

The essential element of the Industrial Doctorates Plan is the industrial doctorate project, that is, a strategic research project carried out at a company that allows the doctoral student to further develop their research training in collaboration with a university, and which is the object of a doctoral thesis.

The Government of Catalonia provides two types of financial support to these projects, based on certain characteristics of project implementation:

o Industrial doctorate projects co-funded by the Government of Catalonia.

o Industrial doctorate projects with specific funding

CHARACTERISTICS COMMON BOTH TYPES

The doctoral thesis will be carried out within the framework of a collaboration agreement between the university (and, if necessary, a research centre) and the company (more than one company may participate).

The candidate will be given a thesis supervisor connected to the university or research centre and a project manager appointed by the company.

The candidate will be selected jointly by the signatories of the collaboration agreement. However, the candidate must be accepted and admitted to the doctoral programme of the corresponding university.

The candidate must have an average mark of 6.5 or higher (scale of 1 to 10) on his or their academic transcript, calculated based on all credits of university studies that allow admission to a doctoral programme.

The thesis supervisor must be part of an active, recognised research group (SGR) of the Government of Catalonia or a researcher from the ICREA programme or be a recipient of European Research Council (ERC) funding.

The time dedicated by the doctoral student to the research projects will be divided between the company and the university.

The candidate will take part in training programmes in specific skills related to RDI project leadership, coordination and management; the transfer of research results; new business development; and industrial and intellectual property, among other relevant subjects.

The candidate will be given a yearly stipend (mobility fund), funded by the Government of Catalonia and accumulative for a maximum period of 3 years. Its purpose is to enable attendance at workshops and conferences related to the research project as well as stays at other company offices, universities or research centres outside Catalonia.

All theses defended under the Industrial Doctorates Plan will receive an Industrial Doctorate with distinction. The participating companies and academic directors will also receive acknowledgement for their participation in the Plan.

APPLICATION PROCESS

Application form dully filled in must be sent to [email protected]

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Predoctoral Position (R1): AI applied to the intelligent validation of distributed automotive architectures through unsupervised learning and environment simulation

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Predoctoral Position (R1): AI applied to the intelligent validation of distributed automotive architectures through unsupervised learning and environment simulation

DESCRIPTION OF THE RESEARCH PROJECT

CANDIDATE PROFILE

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CHARACTERISTICS OF THE INDUSTRIAL DOCTORATE PROJECTS

CHARACTERISTICS COMMON BOTH TYPES

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