The Aeroelastic and Structural Design Lab (ASD Lab) group at the University Carlos III of Madrid (UC3M) is initiating a preliminary selection process to fill several positions in the context of recently awarded research projects with Airbus Defence and international projects financed by the European Commission (Horizon Europe http://indigo-sustainableaviation.eu/, Clean Aviation).
The research will focus on the design of new-generation sustainable aircraft and the development of digital environments enhanced by cutting-edge technologies such as Machine Learning and Multidisciplinary Optimisation. Significant effort will be dedicated to design and optimisation of novel airframe structures developing an automated procedure that combines lower and higher fidelity methods (analytic models, finite element methods) through advanced machine learning algorithms. The PhD position offers competitive salaries, among the highest for doctoral students in Spain, starting from 24,000€/year gross and increasing based on qualifications and objective completion.
We are seeking talented, passionate, and committed individuals with exceptional skills who aspire to lead and become experts in topics that will shape the aeronautic revolution.
We seek candidates who are available in the short term, and in exceptional cases, we may consider waiting for slightly longer durations. Due to the immediate nature of the position, we can only consider individuals already eligible to work in Spain. Please apply only if interested in a PhD position and committed to excellence.
Responsibilities
PhD in Aerostructures
This researcher position in aerostructures aims at developing multi-fidelity design procedure for performance optimisation of new generation green and quite aircraft. The activity will be mainly focused on developing the structural sizing tools defining the airframe internal structures and mass distribution. Different levels of fidelity as well as several optimisation architecture strategies will be assessed and compared in term of accuracy and computational cost. Minimum weight structural optimisation methods will be developed fostering the implementation of advanced composite materials and multifunctional structures. Preliminary weight estimations will be computed by ROMs obtained from both modified advanced beam-based FEM and GFEM-based airframe sizing procedures. AI and machine learning techniques will be applied to generate surrogate models.
Mid/high fidelity GFEM will enhance the procedure accuracy by including design details such as assembly techniques and component joints. Size and free size optimisations will be applied to optimise material distribution and composite ply shape for minimum weight aeroelastic tailoring. Staking sequences internal optimisation will be coupled with size procedure to enable the automatic generation of composite laminates to maximise weight saving while fulfilling ply book rules and manufacturing constraints. Ply drops and thickness variation strategies will be assessed to seek a suitable trade-off between weight saving and manufacturability. The activity of the candidate will include:
- Developing shell-accurate beam element FE model for multi-material hybrid tapered cross-sections
- Defining modelling strategy to include system integration into GFEM
- Developing stress analysis and mass estimation DoE and surrogate models
- Performing minimum weight optimisation
- Developing advanced composite material optimisation
- Developing a strategy to effectively include certification and manufacturing constraints in airframe optimisation routines
- Developing surrogate models based on AI and machine learning
- Using in-house FE codes (Augusto) and commercial software (Nastran, Abaqus, OptiStruct)
- Write reports and deliverables
- Disseminate the work at international technical conferences and greater audience events
- Support R&T activities within ASD Lab
PhD in Multidisciplinary Design and Optimization (MDO)
The activity of the candidate will include:
- Using the State-of-Art approaches, tools and MDO libraries (e.g., AGILE suite https://www.agile-project.eu/open-mdo-suite/, GEMSEO https://gemseo.readthedocs.io/en/stable/, OpenMDAO https://openmdao.org/)
- Integrating the several discipline modules within the optimization workflow.
- Develop give assistance to the development of discipline modules.
- Selecting the most promising MDO mathematical formulations (MDO architectures).
- Launching the optimization campaign and support all related activities.
- Further advancing the SoA of MDO of new generation aircraft
- Write reports and deliverables.
- Disseminate the work at international technical conferences and greater audience events
Qualifications
Hold a MSc (or MSc student with 60 ECTS passed at contract signature) in aerospace engineering or a relevant discipline.
- Students with a background, in aircraft design, aerostructures, FE analysis, composite material, will be prioritised
- Student with a background in aircraft design, Multidisciplinary Design and Optimisation, Aeroelasticity, flight load, Artificial Intelligence/Machine Learning we will be also considered.
- Have an outstanding academic record, critical and creative thinking.
- Be proficient in English (oral and written).
- Deal independently and proactively with scientific and engineering challenges; be self-motivated and capable of working under pressure to meet deadlines.
- Have programming skills (e.g., Python).
Benefits
- Work as part of aeronautical company-funded and European research projects
- Become part of a young, dynamic, highly qualified, collaborative team
- Experience flexible working environment and schedule
- Opportunity to participate in international conferences to present research activities.
- Have health coverage under the National Health System.
- Salary raises and production prices based on performance.
Funding
24.000€-28.000€ gross, p.a., for 3/4 years negotiable based on qualifications and objective completion).
Applicants are expected to apply on LinkedIn uploading a CV and a motivation letter by the 8th of December. For further information, please contact Dr Andrea Cini.
PhD in Aerospace Engineering UC3M 