Our GECKO EU Marie Skłodowska-Curie Innovative Training Network is currently looking for 15 highly motivated early stage researchers (ESRs), of any nationality, to work within 9 European academic and industrial institutions.

As an ESR, you will be trained in an international, inter-disciplinary academic and industrial environment through state-of-the-art research, GECKO training schools, and secondments to academic and industrial institutions. You will enter a 36-month work contract (full-time) with the recruiting institution, be enrolled in a PhD programme, and will also be seconded to other GECKO academic and industrial partners.

Smart technology is everywhere – in our homes, pockets, and networks. Is smarter use of energy essential for low-carbon energy systems of the future? Or is smartness just a buzzword for new gadgets that require ever-more energy and worsen the digital divide? Smart technology is a mass of hopes, fears and contradictions. GECKO will explore interpretable and explainable Artificial Intelligence (AI) models to mitigate unintentional harm to end users caused by the ever-growing spectrum of poorly designed machine learning models, going beyond image and NLP applications.

GECKO PhDs offers a unique opportunity to untangle relations between sustainability, technologies and people. The interdisciplinary ‘GECKO’ network connects world-leading teams of social, computer and information scientists working on smart technology, AI, human-computer interaction, sustainability, climate change, and responsible innovation.

The successful applicants will benefit from a wealth of training opportunities, from secondments to intensive summer schools, as part of a cohort of 15 PhDs networked across Europe. This cohort will form the next generation of applied scientists helping to advance knowledge on pressing policy and technology development issues in the rapidly changing field of smart technology, sustainability and people.

Please find all positions below:

ESR 01 – closed
Towards interpretable ML models for low-carbon technologies
ESR 04 – closed
Adaptable semi-supervised recurrent deep learning and concepts of tensor algebra for extracting meaningful patterns from energy signals
ESR 07 – closed
Influence of smart metering and micro-generation on prosumer’s energy demand
ESR 10 – closed
CHT for low-energy scripting
ESR 13 – closed

Explainable AI (XAI) for smart, domestic technologies

ESR 02 – closed
Co-design of information extraction methods to meet privacy and trust requirements
ESR 05 – closed
Designing CHT for energy efficient and sufficient consumption practices and domestic routines

ESR 08 – closed
Smart building designs and closing the energy performance gap
ESR 11 – closed
Design methodology for user centred perspective of smart grids recognising diversity of user groups
ESR 14 – closed

Responsible ML models that prevent undesirable outcomes for low-carbon CHT

ESR 03 – closed
Evaluation of the responsibility of AI
ESR 06 – closed
Using computational and explainable ML methods to support end consumers to renovate domestic infrastructure
ESR 09 – closed
Smart energy technologies and the energy poor
ESR 12 – closed
Intelligent Personal Assistants (IPA) for energy-related routines
ESR 15 – closed
Implementation of trustworthy ML-driven software solutions for energy demand measures and optimal flexibility aggregation and management strategies