Overview
The transportation sector is one of the largest contributors to European Union (EU) greenhouse gas (GHG) emissions. It is strongly impacted by EU regulations that aim to reduce these by at least 55% by 2030 compared to 1990 levels. This has drastically changed the transportation landscape, especially affecting car and van fleets, shifting from fossil fuel powered internal combustion engine vehicles to plug‑in hybrids or battery electric vehicles. The electrification alone is not enough to meet the goal of a climate‑neutral continent by 2050, as the automotive value chain includes material suppliers and OEMs that heavily influence GHG emissions.
Project Objectives
The main objective of the PhD project is to develop intelligent and technologically innovative solutions that reduce the metal forming industry's carbon footprint and cost. The focus is on extreme loading conditions – high loading rates and elevated temperatures – for EV‑specific and armour components in crashworthiness and impact scenarios. The project will investigate the use of scrap‑tolerant or low‑carbon aluminium alloy products, either stamped or extruded, for EV components.
Funding
This four‑year predoctoral position is fully funded by the Agencia Estatal de Investigación (AEI) through the iSAFE (PID2024‑162078OB‑I00) “Generación de Conocimiento 2024” project. The funding covers tuition fees and a budget for short stays abroad (three months).
Work Program / Duties / Responsibilities
The main tasks include advanced mechanical characterisation of sustainable aluminium alloys and the development of AI‑assisted models. Responsibilities are:
* Assess the feasibility of using low‑carbon and/or scrap‑tolerant alloys.
* Develop experimentally validated, high‑fidelity constitutive models.
* Design more efficient hot forming processes.
* Build mechanics‑informed, data‑driven machine learning models for faster analysis of forming processes and safety assessment.
* Investigate the performance of aluminium protective structures designed for EVs.
* Acquire skills for conventional and advanced testing equipment (universal testing machine, Hopkinson bar system, biaxial hydraulic testing machine).
* Program computational constitutive models.
* Apply AI technologies to mechanical problems in forming and constitutive modelling.
* Attend national and international conferences.
* Publish peer‑review articles in high‑impact indexed journals.
Start of Thesis
The PhD will start within a maximum period of one week after admission by the Academic Committee of the Doctoral Programme.
Contract Type
Researcher in Training contract associated with a predoctoral grant. Duration: 4 years.
Grant Details
The grant amounts for each year are: Year 1 & Year 2 € 23 476,48 gross; Year 3 € 25 153,37 gross; Year 4 € 31 441,72 gross. Beneficiaries are exempt from doctoral program tuition and thesis reading fees. The grant must be renewed annually after verification of adequate performance.
Requirements
Educational requirements: Master’s degree (or about to be completed) in Mechanical, Civil, Aerospace Engineering, Materials Science and Engineering, or a related field.
Specific knowledge: strong background in solid mechanics, including mechanics of materials; capacity to work in an interdisciplinary and international environment; self‑motivation and willingness to perform research; creativity in problem solving; ability and eagerness to learn new skills outside one’s discipline.
Languages: high level of written and spoken English (C1 or equivalent).
Plus: programming skills (Python, FORTRAN, Matlab); machine learning and finite element‑based physics simulation knowledge.
#J-18808-Ljbffr