The biggest challenge of aviation today is to drastically abate its growing environmental impact. Aviation is the second biggest source of transport emissions after road transport and at the same time is the fastest-growing source of greenhouse gas emissions. Specifically, if global aviation were a country, it would rank in the top 10 emitters. As a result, sustainability improvements in aviation, are essential for tackling climate change and the resulting ecological crisis.

Artificial Intelligence (AI) is proving to be a real game changer since AI models have achieved promising results toward clean aviation. However, these achievements come at a cost: they are not yet sustainable. In 2019, researchers at the University of Massachusetts Amherst analyzed various AI natural language processing training models in order to estimate the energy cost in kilowatts required to train them. Converting this energy consumption into approximate carbon emissions and electricity costs, the authors estimated that the carbon footprint of training a single big language model is equal to around 300,000 kg of carbon dioxide emissions. This is of the order of 125 round-trip flights between New York and Beijing for one person. Therefore, moving towards clean aviation, sustainable AI models must be developed first.

To that end, the research in this Ph.D. project will be on developing novel adaptable physics-informed prognostic models for aeronautical structures and systems so as to transform 'power-hungry' models into 'power-sustainable' models. Towards a physics-informed model, the user will be able to engage physical meaning and replace parts of the training process that will allow better understanding and interpretation of the results, while maintaining a high level of learning and performance. Furthermore, the related CO2 emissions will be reduced since less learning data will be needed, and as a result, the computational effort of the training process will be drastically decreased. Finally, utilizing adaptation characteristics the physics-informed model will be able to learn from unexpected phenomena during the operation and adapt its estimated parameters so as to obtain more accurate and reliable prognostics, along with decreasing the needed learning data. 

Applicants should have:

• MSc degree (or equivalent) in statistics, applied mathematics, or engineering and have a strong background in machine learning.
• Good programming skills e.g. Python, C, R.
• Background in the field of stochastic modeling, such as Hidden Markov Models.
• Background in the field of Bayesian statistics.
• Ability to work in a project team and take responsibility for own research goals.
• Very good communication skills in English, both written and oral.

The candidate is expected to contribute to an exciting and vibrant atmosphere in the Department of Aerospace Structures and Materials, and publish work of high scientific quality and participate in international conferences

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

Fixed-term contract: 4 years.

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3247 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Aerospace Engineering

The Faculty of Aerospace Engineering at Delft University of Technology is one of the world’s most highly ranked (and most comprehensive) research, education and innovation communities devoted entirely to aerospace engineering. More than 200 science staff, around 250 PhD candidates and over 2,700 BSc and MSc students apply aerospace engineering disciplines to address the global societal challenges that threaten us today, climate change without doubt being the most important. Our focal subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems. Working at the faculty means working together. With partners in other faculties, knowledge institutes, governments and industry, both aerospace and non-aerospace. Working in field labs and innovation hubs on our university campus and beyond.

Click here to go to the website of the Faculty of Aerospace Engineering.

To find out more about the department please visit this link.