Graduation project: Data and text Mining for disaster funding at the Red Cross
As a Graduate Intern at ORTEC you will be part of a unique organization and team! With this graduate project, you will be part of ORTEC’s student team in the Text Mining LAB. Together with other graduate students, you will work within the same strategic topic, but you will have your own research question and thesis subject. Within this LAB you will have the possibility to brainstorm and discuss with colleagues about the topic and everything that is encompassed in writing a thesis, leading to a thesis with more impact.
This assignment is a collaboration between ORTEC and 510. 510 is a self-organizing data innovation initiative of the Netherlands Red Cross. Their vision is to shape the future of humanitarian aid at global level by the smart use of data. Contributing to open data, data analysis and capacity building in governments and NGOs are essential to increase the understanding of humanitarian data. Applying data science can aid humanitarian relief workers, decision makers and people affected, to better prepare for and cope with disasters and crises.
Every year, many small and medium-sized disasters occur in silence, without the attention of the mainstream media. Without visibility or attention beyond the local region in which they occur, these silent disasters often do not receive the influx of financial support that can follow large-scale emergencies or disasters. To support these smaller emergencies or disasters, or to provide initial funding before emergency appeals are launched for large-scale disasters, the International Federation of Red Cross secretariat allocates grants from its Disaster Relief Emergency Fund (DREF) to National Societies to support their operations. All requests for DREF allocations are reviewed on a case-by-case basis. In relation to the DREF the following documents are produced: appeals, plans and updates. These documents are available through the following repository:
The aim of this Master Thesis is to widen the application scope of DREFs documents for disaster response and preparedness in order to better shape and target future interventions. Data- and specifically text mining should be employed to semi-automatically extract data from DREF reports, by analyzing ways to combine this data with open, public risk data and provide recommendations as to how to structure future digital DREFs.
Several research questions are important in this research:
This master thesis gives you the change to work at both ORTEC and the Red Cross location in The Hague, which means you get an internship on the state of the art in humanitarian aid combined with the state of the art in Data Science. You will be coached by Dr Marc van den Homberg, the scientific lead of 510 and Ronald Buitenhek, the lead of the Center of Excellence Machine Learning at ORTEC.
If you are interested, we would like to receive your resume, motivation and grade list. You can send your application to email@example.com. As a student you can complete your master thesis and/or work part-time as a student assistant, in this case for both ORTEC as the Red Cross. If you want to know more about the possibilities or need more information, then please contact Gordon Boon (Recruiter).
Who you are
What we offer
What to expect
We help you to thrive in your field of expertise. We operate a flat organizational structure that keeps communication lines short. The atmosphere is open, informal, cooperative and positive. We employ over 900 people in the Netherlands (HQ), Belgium, Germany, France, the U.K., Romania, Italy, the U.S., Australia, Brazil, Poland and Denmark.
Visit our website www.ortec.com to learn more about our solutions and clients’ experiences.
Acquisition as a result of this vacancy is not appreciated.