Sort by
Refine Your Search
-
coordination failures reshape decarbonization pathways. Your research will combine methods from network analysis and agent-based modelling of economic systems to trace how international material and financial
-
, the ability to analyze complex problems, manage your time effectively, innovate and stay resilient under pressure. Combined with the ability and willingness to work independently and collaborate well
-
PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Positive tipping points in the innovation and diffusion of clean energy technologies can greatly accelerate progress towards a net-zero energy system. Yet, their emergence and timing remain difficult
-
climate resilient policies! Job description This 4-year fully funded PhD position is part of the ERC Consolidator project “Systemic physical climate risk in complex adaptive economies” (SPHINX). The SPHINX
-
an important contribution to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber) security. TPM does this with its excellent education and
-
) in the EU training network EXPLORA EXPLORA is a Marie Skłodowska-Curie doctoral network funded by the HORIZON 2020 framework. It will start on 1 February 2026, and within this network we have two
-
. This simple unit is limiting the learning capabilities of recurrent neural network models in tasks characterized by multi-timescale and long-range temporal dependencies. To implement multi-scale adaptation, in
-
of mixed fixed-flexible transport networks? Job description The increase of public transport usage has clear potential in transforming our environment to be more liveable, sustainable and convenient. However
-
AI-driven solutions for sustainable, efficient, and collaborative port operations of the future. Job description European seaports must achieve net zero emission by 2050 and 55% emission reduction
-
make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods