Marie Skłodowska-Curie PhD grant in the FutureArctic Innovative Training Network
The EU-funded Innovative Training Network FutureArctic aims to quantify how much carbon will escape from the Arctic in future climate. How do the multitude of ecosystem processes, driven by plant growth, microbial activities and soil characteristics, interact to determine soil carbon storage capacity? A group of fifteen PhD-students will study the Forhot ecosystem in Iceland, where a natural coincidence has provided us with the exceptional opportunity to actually look into the future.
Given the strong urgency of tackling and managing the climate challenge and the particularly important role herein of (sub)Arctic ecosystems, a rapid assessment of the ecosystem and ambient processes in this natural laboratory is essential. FutureArctic will achieve this challenge by adopting the fast advances made in the field of machine learning and artificial intelligence (AI), unmanned aerial vehicles (UAV) and (remote) sensor technology into environmental research at the ecosystem scale, into a new concept of an ‘ecosystem-of-things’.
FutureArctic thus aims to channel an important evolution to automated machine-assisted fundamental environmental research. This is achieved through dedicated training of researchers with profiles at the inter-sectoral edge of computer science, artificial intelligence, environmental and agricultural science, sensor engineering and communication and social sciences. FutureArctic training ensures the development of unique enviro-technological job profiles, all with their own specialty, embedded in holistic knowledge on connected high-data throughput ecosystem research, ready for machine-assisted environmental ecosystem science and modelling.
About the host organization
imec is the world-leading research and innovation hub in nanoelectronics and digital technologies. The combination of our widely acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique. By leveraging our world-class infrastructure and local and global ecosystem of partners across a multitude of industries, we create groundbreaking innovation in application domains such as healthcare, smart cities and mobility, logistics and manufacturing, and energy.
University of Antwerp – imec IDLab Research group
The IDLab research group of imec and the University of Antwerp performs fundamental and applied research on internet technologies and data science. The overall IDLab research areas are machine learning and data mining; semantic intelligence; distributed intelligence for IoT; cloud and big data infrastructures; multimedia coding and delivery; wireless and fixed networking; electromagnetics, RF and high-speed circuits and systems. Within Antwerp, IDLab specifically focuses on wireless networking and distributed intelligence. IDLab has a unique research infrastructure used in numerous national and international collaborations.
IDLab collaborates with many universities and research centres worldwide and jointly develops advanced technologies with industry (R&D centers from international companies, Flanders’ top innovating large companies and SME’s, as well as numerous ambitious startups).
Your PhD project
State-of-the-art artificial neural networks have achieved remarkable successes in terms of accuracy in a variety of AI-related tasks. Part of this success is due to the availability of huge amounts of (labeled) data and ample computing power for off-line training.
However, in many real-life applications such as UAV-based remote sensing there is a need to be able to train or online adapt these networks with only limited amount of labeled data, and robust networks that can be executed on small and low power devices.
In this research project, you will investigate new brain-inspired ideas that go beyond the current 2nd generation networks (e.g. spiking neurons, temporal coding, learning with hyper-vectors, …) with the aim of designing solutions with high accuracy, but that can also learn new concepts from only a limited number of examples, are robust to noise, and allow for low power implementations
You will embark on secondments to other FutureArctic partners (UNIVIE and UAntwerpen) to integrate the learning algorithms and eco-algorithms to develop sustainable development applications and to get hands on insight into the problem and the ecosystem-scale eco-science: learning the functioning of a subarctic grassland.
Benefits of working in an ITN