Internship | Various Internships

1 maand geleden

Delft, Nederland TNO Voltijd

What will be your role?

Introduction
Within TNO, you will work in the Digital Built Environment department of the TNO Mobility & Built Environment unit. This is a new and growing department with an enthusiastic dynamic. The department consists of about 35 colleagues. A social and committed group of colleagues with diverse backgrounds, experience levels and nationalities. The common ground is digitalization in which we cover various aspects of it at expert level. Think ontology, Linked Data, BIM, data-science, software engineering and development, and standardization. Our tools, standards, and expertise (of construction processes, for instance) have been the basis for many sustainable improvements in the sector. To this end, we work intensively with the other TNO expert groups within our unit MBE and often collaborate with colleagues from other units of TNO.

Your research is one of the links in the research programs of TNO. Within TNO you have access to modern research facilities (software tools, laboratories, etc.) and an extensive network. Depending on the subject, you will choose a mentor who will help you with your graduation project. We offer you the opportunity to develop yourself as a specialist, and to present and publish your research where possible.

Since digitalization plays a central role in various domains such as construction, civil infrastructure, and greenhouses we offer a variety of topics for you to work on. Each topic described below requires different expertise and knowledge and offers lots of opportunities to develop yourself in close collaboration with our colleagues.

Good to know: for some of these topics you do not need to have any background in construction, civil infrastructure, or the greenhouse sector. Of course, if you do, that's a bonus, but surrounded by our experts, you'll learn quickly.

What you will be doing
Topic 1: End of life rule checking for supporting prioritization of Replacement and Renovation Decision Making of Rijkswaterstaat through knowledge graphs

This topic is providing a digital solution to tackle four challenges: Ageing Infrastructure, Missing Overview of Large Population of Infrastructure Objects in relation to their object-type-specific failure mechanisms, Scattered data and limited utilization and understanding of technical data to strategic asset management, Limited re-use of existing experiences and knowledge on past failures.

The solution we work towards is creating a Rule-Based Model that is based on knowledge graphs. There are system components such as schemas, instantiation, rule-sets, rule-checking and visualization. Schemas include ontology design to capture semantic domain knowledge as well as linking and re-using existing ontologies. Schemas will be used for automatic instantiation of objects with open-source framework such as Apache Jena or RDF Lib. Rule-sets will be based on SPARQL, SHACL will be used for rule prototyping, and a GraphDB will be used for rule checking and visualization. Any experience with the abovementioned topics and also with UI connected to GraphDBs is additionally appreciated.

Topic 2: Development of physics-enhanced machine learning models for detecting damage at bridges

The aging civil infrastructure in the Netherlands poses severe challenges regarding the replacement and/or renovation of structures that reach their end-of-service life. A typical example constitutes the Dutch bridges which encapsulate multiple complications regarding safety-related consequences as well as highly expensive and time-consuming computational models to accurately arrive at maintenance strategies. Machine learning methods offer an efficient alternative to the traditional detailed physics-based models; However, they rely heavily on the amount of data available.

To this end, TNO is exploring two approaches to circumvent the large data dependency. On the one hand investigating the benefits of incorporating physics in such models to reduce the dependency on large amounts of data. On the other hand, TNO is building exact Predictive Twin models, such as a real bridge, which can facilitate generating large amounts of new real-life sensor data. By combining the two methods, sufficient data can be generated for the reduced data dependent models. With the aid of such a model, more accurate and realistic predictions can be made about the remaining service life of a structure and a projection of its current deterioration in a much shorter time frame.

A plethora of interesting avenues exists for solving such a problem formulation, such as investigating the most efficient way to include physics in machine learning models (Physics Informed Neural Networks, Variational Autoencoders, etc); Addressing and quantifying the ever-present uncertainties in such high-complexity physical systems in an efficient and accurate manner (coupling existing state-of-the-art machine learning algorithms with Bayesian principles); Validating these machine learning models using predictive twins, quantifying how well they describe reality and estimating the reliability of their predictions.

What we expect from you

We offer a graduation position to students that:

Follow a master program in STEM such as civil engineering, mechanical engineering, architecture, (applied) physics or mathematics, computer science, or similar. Have an investigative and critical attitude, you want to know why certain phenomena occur and determine its effect. Like to cooperate with others and are creative. Live in The Netherlands. Have excellent oral and written communication skills in English, and preferably Dutch language (B1 level).

Of course, for each topic described above we may expect additional knowledge and skills depending on the research topic you are interested in.

What you'll get in return

You want an internship opportunity on the precursor of your career; an internship 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. Furthermore, we provide:

A highly professional, innovative internship environment, within a team of top experts. A suitable internship allowance ( euro for wo-, hbo- and mbo-students, for a full-time internship). Possibility of eight hours of free leave per internship month (for a full-time internship). A free membership of , where you can meet other TNO professionals and join , such as sports activities, (work-related) courses or the yearly ski-trip. Use of a laptop. An allowance for travel expenses in case you don’t receive an OV-card.

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