What will be your role?
The explosive growth of network-connected and -operated sensor devices has caused an even larger growth of available sensor information.
Often, this data has to be transferred to a central (cloud) environment for further processing, which introduces a strain on network capacity.
This is especially problematic in resource constrained environments where bandwidth is limited and network connection can not be guaranteed.
In these cases, edge nodes will need to make decisions themselves about what information to send over these limited connections, and what information to process locally (or even discard).
At the same time, we see today that processing capabilities at the edge are increasing, and even forming local federated clusters, making it feasible to run more advanced processing closer to the sensors.
Methods such as abstractive summarization and captioning (e.g. in the NLP and imaging domain) 1 , attention-based RL methods 4 , value-of-information analysis 2 3 , intelligent early discard 5 , or plain compression could be put to use to decide (1) what pieces of information carry most value and are most critical to propagate further in the network, and (2) distill that information for transfer over constrained connections.
Of course, the value of information may vary depending on the situation and the receiving user’s / system’s current requirements.
For this master thesis research, we are interested in both (1) new methods and algorithms, and (2) supporting cloud-native architectures, for adaptive information processing, ultimately improving the speed of information delivery and efficient usage of network resources.
We are looking for students interested in working on either of these challenges, both on adaptive system architectures, and on the algorithms that can be incorporated in such a system.
We ask the student to think outside the box, and explore new approaches towards an adaptive information processing system.
Examples of research questions that come to mind are the following, though we encourage the student to introduce his own :
How to determine what data should be given priority in communications?
What interfaces and abstractions are required to expose the necessary information regarding network quality and data processing needs to the above methods?
What will you be doing?
In this position you will be working together with researchers and SMEs in the (federated) cloud domain on a challenging internship assignment, hopefully resulting in a publication.
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.
This internship gives you an opportunity to take a good look at your prospective future employer. 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. Ideally, the start date would be in early September, but this can be aligned with the applicants wishes to some degree.
What we expect from you
You are an ambitious master student with both scientific and practical skills (designing solutions, programming, experimental validation, and presenting your ideas and work).
You are fascinated by technology and eager to learn new technologies or related aspects . You strive for an excellent thesis result and you are at least available for 6 months of (fulltime) work at TNO.
You have a background in Artificial Intelligence, Computer Science, or related, with a special interest in combining AI / ML with cloud (edge / fog) technology / distributed system engineering.
You are pro-active and independent, and at the same time a good team player. Affinity with cloud native technologies and the Defense domain is a plus.
Good communication skills (in English) is expected.
What you’ll get in return
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.
TNO as an employer
Monitoring & Control Services
We, at Monitoring & Control Services (MCS) prove the feasibility as well as the added value of innovative applications of information technology for industry and government through an approach of first-time engineering of large scale monitoring & control systems.
We view such systems from an Internet-of-Things perspective, where large amounts of heterogeneous data are generated by internet-connected infrastructures.
These data need to be stored, processed and analyzed to build useable information for decision-making or autonomous control.
Our research group deals with the information technology challenges involved in :
1.Automatic acquisition, storage and retrieval of large volumes of data across organizations.
2.The continuous processing of large distributed sets of data into information using adaptive (machine-learning) algorithms from multiple parties involved in Big Data analysis.
3.Predictive analytics and large scale simulation of complex model chains for decision support in different phases (from planning / design to maintenance) of the innovation cycle.
4.Humans interacting with the processing of data into information within complex systems.
By developing future-proof, adaptable and scalable IT solutions, we cooperate with government bodies, enterprises, service providers and non-governmental organizations in three focus domains - Industry & Infrastructure, Sustainable Energy, and Defense to create a more efficient, greener and safer tomorrow.
We are a fun and very social group of applied researchers, consultants in IT systems and processes, as well as a number of System Integration experts, located in Groningen and the Hague.