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. In this PhD project, you will: Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty. Design synchronization, consolidation, and matchmaking
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. The PhD candidate will combine materials science, computational modelling to design a novel framework that identifies optimal materials and AM processes based on performance, sustainability, and reusability
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of the MagHeat Project As the PhD candidate for this position, you will be at the very core of the MagHeat project, leading the work on “Design and development of a magnetic refrigerator with 10 K
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quantum chemistry to logistics, energy systems, and AI-driven optimization. These problems are widely regarded as the natural domain of quantum computers, yet they remain extremely demanding for both
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a highly motivated PhD candidate to advance predictive, data-driven production and
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spaces for applications. To optimize tACS towards a technique of network stimulation in the human brain, we use computational modeling at the population level (neural mass models) as well as at the neuron
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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. Background: You hold a PhD in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems or related fields. Core Expertise: Strong expertise in Federated Learning and/or Continual Learning
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hardware development and system-level optimization. Where to apply Website https://www.academictransfer.com/en/jobs/356825/postdoc-position-on-power-elect… Requirements Specific Requirements PhD in