Internship | Derivation of new wave pressure formula using mechine learning regression

Will you help TNO in developing the wave pressure formula of the future?



Education type

university (wo)


Internship and graduation project

Hours a week

Fulltime – 40


Apply now


What will you be doing?

Slender vertical hydraulic structures such as sluices or navigation locks are often loaded by wave loads. The adequate determination of the wave pressures exerted upon these structure is of utmost importance; both to ensure sufficient structural safety and to ensure economic design. Despite their importance, commonly applied wave pressure formulas have multiple limitations.

They are either:
  1. derived on the basis of (theoretical) assumptions which may not always hold in reality;
  2. derived decades ago using relatively small data-sets (below 1000 waves) and not incorporating the vast amount of experimental data we accumulated since then or;
  3. derived with the focus on the accurate prediction of resultant wave-loads, rather than the accurate prediction of wave pressures.

Aim of the MSc. thesis is to explore the potential of (physics-informed) machine learning for the development of a more accurate / generally applicable wave pressure formula for slender hydraulic structures.

During your thesis, you will:
  1. collect and process a large amount of experimental wave pressure data;
  2. train and compare various machine learning algorithms to predict wave loading with gradually increasing complexity;
  3. compare the trained models with currently used models/formulas;
  4. explore how known physics could be incorporated into the machine learning models;
  5. quantify the prediction uncertainty of the selected best performing model;
  6. quantify the degree of extrapolation when evaluating unseen data points; (vii) learn how to perform, document, and present your own research.

What do we require of you?

  • You are following a master program in Civil Engineering with specialization in Hydraulic Engineering, or any other program with comparable focus. Computer science students with experience in machine learning regression and interest in learning about hydraulic engineering are also encouraged to apply.
  • You have at least a basic knowledge on solid and fluid mechanics (you probably know enough if you completed the following courses: Structural mechanics 1-4, structural dynamics, Ocean Waves: CIE4325)
  • You have demonstrated skills in at least one computer language, preferably with a focus on numerical computation, e.g. Python, Matlab, Julia, R.
  • Knowledge and experience in mathematical statistics and machine learning is a plus.

What can you expect of your work situation?

TNO is an independent research organisation whose expertise and research make an important contribution to the competitiveness of companies and organisations, to the economy and to the quality of society as a whole. Innovation with purpose is what TNO stands for. With 3000 people we develop knowledge not for its own sake but for practical application. To create new products that make life more pleasant and valuable and help companies innovate. To find creative answers to the questions posed by society. We work for a variety of customers: governments, companies, service providers and non-governmental organisations. Working together on new knowledge, better products and clear recommendations for policy and processes. In everything we do, impact is the key. Our product and process innovations and recommendations are only worth something if our customers can use them to boost their competitiveness. You will work closely with experts in hydraulic engineering, structural reliability, and machine learning at TNO, Deltares, and TU Delft.

Conditional on good progress, we expect to publish one international journal paper based on the results of the MSc thesis and to present the results to Directorate-General for Public Works and Water Management (Rijkswaterstaat).

What can TNO offer you?

You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.

Has this vacancy sparked your interest?

Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.

Contact: Nadieh Meinen
Phone number: +31 (0)6-452 50684

Note that applications via email and third party applications are not taken into consideration.


Apply now



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