PhD Position Atmospheric Turbulence Modelling Using Physics Informed Machine Learning

Updated: 3 months ago
Deadline: 31 Jan 2024

Are you passionate about exploring the crossroads of machine learning, atmospheric science, and laser satellite communications? If so, we invite you to apply for this exciting research position!

Laser satellite communications holds tremendous potential for global telecommunications access. However, the impact of atmospheric turbulence on laser beam propagation presents a significant technological challenge. This obstacle can be addressed through the implementation of adaptive optics and geographic diversity. Our project's primary objective is to create a comprehensive map detailing the optical channel performance across Europe. This map is crucial for designing ground network technology and estimating communication service availability. Due to the current limitations in understanding and estimating channel performance, we will pioneer novel physics-informed machine learning algorithms to formulate an optical link performance map.

This groundbreaking project comprises two PhD positions and one postdoc position, led by experts in meteorology, machine learning, and laser satellite communications. The focus of this particular PhD position is quantify the atmospheric turbulence at a high spatio-temporal resolution over a vast area and a long period of time. Traditionally, several empirical parameterizations are used to represent the strength of optical turbulence. However, these parametrizations do not account for important factors including geographical location, local meteorological conditions, and temporal effects such as the time of the day. These simplifications of complex real-world atmospheric effects represent the major barriers to obtaining representative laser satcom channel models. We aim to address these challenges via Physics-Informed Machine Learning (PIML), integrating the knowledge of the underlying physical processes into PIML algorithms. To this end, we will create a state-of-the-art physical baseline model with limited spatial and temporal range and collect observational data. Next, we will develop PIML algorithms to obtain the turbulence strength based on existing datasets at astronomical sites to extrapolate the vertical 𝐶𝑛2 profiles to Europe. Those PIML models will take vertical and temporal statistics into account. We will further improve those models by incorporating volumetric correlations, which are fetched from the physical baseline model, and by adding in-situ data from metrology sites. Through the development of the PIML models, we will include additional constraints using physical relations from the state-of-the-art weather model, which is expected to yield more reliable 𝐶𝑛2 profiles. The models will be trained on weather data both from reanalysis data and from in-situ measurements at astronomical locations.

This PhD opportunity offers a stimulating and challenging experience, encompassing both theoretical and experimental aspects. It explores two cutting-edge disciplines— atmospheric science and machine learning—within the context of laser satellite communications, with potential applications extending to fields like astronomy.     

Must-haves:

  • MSc in machine learning, electrical engineering, computer science, mathematics, or equivalent
  • Strong analytical skills
  • Proven record in programming skills
  • Proficiency in English
  • Good communication skills
  • Ability to work in a multidisciplinary, highly dynamic environment

Nice-to-haves:

  • Background in machine learning or atmospheric science
  • Affinity with experimental data analysis

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 .

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 € 2770 per month in the first year to € 3539 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 a monthly work costs contribution. Flexible work schedules can be arranged. 

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme  for partners and they organise events to expand your (social) network.

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!

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here  to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

For more information about this vacancy, please contact Justin Dauwels [email protected].

For information about the application procedure, please contact our secretary Laura Bruns; [email protected].

Are you interested in this vacancy? Please apply before January 31, 2024 via the application button and upload:

  • Curriculum Vitae
  • Motivation letter
  • Letters of recommendation
  • Recent publications
  • List of courses + grades

A pre-employment screening can be part of the selection procedure.

You can apply online. We will not process applications sent by email and/or post.

Please do not contact us for unsolicited services.



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