Internship Wind Turbine Noise Data Analytics
Job Description
What are you going to do?
Arcadis creates unique insights for developers and operators of wind turbines, residents, authorities and other stakeholders. We developed an interactive app for residents near wind farms to create more insight into wind turbine noise. The app uses meteorological models to create short term wind turbine noise, shadow flicker and power forecasts. The aim of the app is to build trust and acceptance by residents through good and transparent communication. By determining under what circumstances nuisance occurs, how it manifests itself and how serious it is, we tailor solutions and make a more efficient noise management and communication strategy possible.
Our team offers the market a unique added value through the combination of the following services:
- The use of the interactive app Geluidsverwachting.nl and the associated client dashboard.
- Noise source measurements on wind turbines.
- Continuous monitoring of noise and meteorological conditions.
- Data analyses that create new insights by combining all types of data.
The research internship offers a broad range of activities. It is mainly aimed at the further optimisation and validation of the noise forecast app, analysis of wind turbine noise measurements, tonality assessments and the development of new related applications like Power BI dashboards. The internship includes analysis of large data sets of wind turbine Scada data, noise measurement data, feedback data from residents, meteorological data and improving or adding features to the noise and shadow flicker forecast script. Noise measurements at wind turbines or at residential areas could also be part of the internship. We are looking for a creative, open-minded analyst who can identify patterns in the data and sees opportunities for improvement and innovation.
Will you help us to further optimize and innovate our tools and services regarding wind turbine noise?
What do we ask?
For our 4 to 6 months Internship Wind Turbine Noise Data Analytics for the wind industry we are looking for a student with the following competences:
- Bachelor/Master student with a study related to applied physics, aerospace engineering, sustainable energy technology, meteorology, mechanical engineering, artificial intelligence or big-data analytics;
- Experience with programming and data analytics in Python;
- Strong analytical skills;
- Independent work ethic;
- Strong sense of purpose, enthusiastic and eager to learn;
- Good English oral and verbal skills are essential;
- Experience with meteorological models is an advantage.
What do we offer?
Wherever you work, whatever you do, as an Arcadian you contribute to our mission: ‘Improving quality of life’. This mission is what connects and inspires us. We shape the future from different perspectives and build sustainable relationships. Together, we want to improve our world. With our five core values we build our Arcadian culture: People First, Integrity, Client Success, Collaboration and Sustainability. Characteristic for our culture are inclusiveness, diversity and corporate social responsibility. These subjects can be seen throughout our different activities
Arcadis offers you a whole range of interesting internships and graduation projects, which content will be determined together with you. At Arcadis, we value flexibility, which means you’re free to rearrange the workhours and work locations of your work activities as you see fit.
You will also get to know your colleagues through participation of activities like those provided by: The Young Part, our young adult association. Many interns/graduate students grow into a junior position and they also receive benefits like monthly compensation. For more information on Arcadis, visit our website.
Contact information
If you have questions, please contact Ms. Sanne Bruinsma, campus recruiter, over email [email protected]. You can apply by using the ‘Apply Now’-button on this page. You cannot apply by email. #LI-HLB stage afstuderen internship #internshipNL
