PhD Student in physics-informed graph neural networks for composite space structures
Mission
EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries. The IMOS Lab (https://www.epfl.ch/labs/imos/ ) offers a highly motivating, interdisciplinary scientific environment with many opportunities to interact between different projects and researchers, and has an excellent network of collaborations with industrial stakeholders and other international universities.
The objective of this project is to develop a novel methodology that develops physics-informed graph neural networks (GNNs) for composite space structure analysis. The methodology will aim to leverage the inherent structure of composite materials, embedding physical laws and multi-physics constraints into the GNN framework. This approach will enhance predictive accuracy, reduce computational costs, and facilitate the design, simulation, and optimization of high-performance composite space structures, particularly for aerospace applications. Validation of the developed models through experimental data and collaboration across disciplines will ensure the robustness and real-world applicability of the proposed solutions.
This PhD position is part of the Marie Skłodowska-Curie Actions (MSCA) program, which aims to provide high-quality research training and career development opportunities for early-stage researchers in Europe. Several secondments are foreseen as part of this PhD position.
Profile
We are looking for a PhD candidate with a strong analytical background, and an outstanding MSc degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in geometric deep learning, signal processing, statistics and learning theory. We expect the candidate to be self-driven with strong problem-solving abilities and out-of-the-box thinking. Professional command of English (both written and spoken) is mandatory.
In accordance with the eligibility criteria of the Marie Skłodowska-Curie Actions (MSCA) program, candidates must not have resided or carried out their main activity (work, studies, etc.) in Switzerland (CH) for more than 12 months in the three years immediately prior to the recruitment date.
Application Process
The application file should contains:
· a letter of motivation,
· a CV of the candidate,
· brief research statement (one page) describing your project idea in the field of physics-informed deep learning algorithms, making connection to your experience in this area and the related work from the literature,
· transcripts of all obtained degrees (in English),
· one publication (e.g. thesis or preferably a conference or journal publication, providing a link to the publication is sufficient).
Informations
Application deadline: 15.10.2024
Further information on EPFL IMOS Lab can be found under: https://www.epfl.ch/labs/imos/
Activity Rate : 100%
Contract Type: PhD Student
Duration: 1 an, renouvelable
Reference: 1062
Shortlisted candidates will be invited to apply to one of the EPFL doctoral schools (e.g. EDEE). This parallel application process is necessary to be eligible for a PhD at EPFL.