FRAUNHOFER INSTITUTE FOR APPLIED INFORMATION TECHNOLOGY FIT
CALLING ALL FUTURE EXPERTS* IN FAIR DATA AND DATA DRIVEN SCIENCE! MAKE THE MOST OF YOUR TALENTS AT FRAUNHOFER FIT IN AACHEN. JOIN OUR FAIR DATA AND DISTRIBUTED ANALYTICS GROUP AND WORK ON BUILDING FAIR DATA INFRASTRUCTURES. WE HAVE AN OPENING FOR A:
Fraunhofer is Europe’s largest application-oriented research organization. Our research efforts are geared entirely to people’s needs: health, security, communication, energy and the environment. As a result, the work undertaken by our researchers and developers has a significant impact on people’s lives. We are creative. We shape technology. We design products. We improve methods and techniques. We open up new vistas.
The Fraunhofer Institute for Applied Information Technology FIT is an excellent partner for the human-centered design of our digital future. Being a driver of innovation, we provide guidance and shape the digital transformation of industry, environment and society. We work in an independent, scientifically informed and creative way.
Digitalization and data driven economy require new approaches and methods to improve data reusability. A particular challenge is to enable the seamless use of big distributed data resources by applications and analytic processes in a way that guarantees data owners’ sovereignty. For this purpose, Fraunhofer has developed the International Data Spaces (IDS) architecture and related approaches for distributed analytics and is contributing to the federated European data infrastructure GAIA-X.
Your tasks include
Your tasks will include developing new concepts and components of a data reuse framework for IDS that is aligned with the FAIR principles to make data “Findable, Accessible, Interoperable, and Reusable” both for humans and machines. The position will have a research focus on applying persistent identifiers and knowledge graphs in the IDS infrastructure. Advanced data science and web technology will be transferred to innovative solutions for data management in research infrastructures and industry.
Your focus in this position will be to design and implement infrastructure components and supporting tools. You will be responsible for designing and developing a data reuse infrastructure to meet the growing demand of big data solutions to be deployed in distributed computing and cloud infrastructures at scale.
What we expect from you
You have at least a bachelor’s degree in Computer Science or a related field as well as in-depth knowledge and work experience in the following areas:
What you can expect from us
We practice a family-friendly culture: we know that sometimes family life takes precedence. We provide a parent-child office, emergency care, holiday care, and consulting services for home and eldercare. We support a healthy work-life balance by flexible working hours and part-time models.
Appointment, remuneration and social security benefits based on the public-sector collective wage agreement (TVöD). Additionally, Fraunhofer may grant performance-based variable remuneration components.
The position is initially limited to 2 years.
This vacancy is also available on a part-time basis.
In case of identical qualifications preference will be given to severely disabled candidates. We would like to point out that the chosen job title also includes the third gender. The Fraunhofer-Gesellschaft emphasises gender-independent professional equality.
Please submit your online application via:
If you have any questions regarding this position, please contact:
Ms. Oya Deniz Beyan
Phone: +49 241 80-21512
Additional information is available at:
|Title||Data Engineer* / Software Developer* – Data-based Services|
|Employer||Fraunhofer FIT, Sankt Augustin|
|Job location||Schloss Birlinghoven, 53754 Sankt Augustin|
|Published||September 16, 2020|
|Job types||Engineer,   Research assistant  |
|Fields||Informatics,   Computing in Mathematics, Natural Science, Engineering and Medicine,   Databases,   Distributed Computing,   Information Systems (Business Informatics),   Software Engineering,   Big Data,   Machine Learning  |