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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
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, 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
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. 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
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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
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loads, inclusive markets and technology, complex ownership, and power assets connected via power electronics. New and unsolved phenomena such as swarm behaviour of smart power prosumers (consumers and
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position, you will lead the development of a probabilistic, error-aware surrogate model capable of delivering fast, uncertainty-quantified predictions for complex multiscale–multiphysics processes in OFPV
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systems such as supply chains, transport, energy grids, and water networks. When floods, pandemics, or other disruptions hit, these support systems can fail, creating cascading risks: ambulances can’t reach
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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
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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
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resilience through responsible risk management. Research areas include critical infrastructure protection, systems resilience, crisis governance, uncertainty in complex systems, and the interface between