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Field
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scale radar datasets with high quality labels remain scarce. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations
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is not just about writing three papers and moving on. It is about building a profile that sits at the intersection of energy systems, AI & modelling and optimization & control. That combination is rare
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-level power, thermal and energy-efficiency models for future heterogeneous computing systems built on advanced CMOS technologies What you will do Continued technology scaling and the integration
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. Adapt to GNNs the interpretability tools created for large language models, such as sparse autoencoders, to locate the variables that the network represents internally in nodes and edges. Design causal
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Particle Fluid Dynamic (CPFD) model development. The person appointed will be affiliated with the Research Group in Energy and Environmental Technology (URGENT). Duties Complete the doctoral programme up
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: Developing a system-theoretic framework that models learning as the feedback interconnection between continuous-time circuit dynamics and optimization algorithms. Designing novel energy-based learning
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the vascular organoid model, to establish a causal link between these target genes and the vascular calcification phenotype. This PhD project, at the interface of vascular biology, immunology, stem cell biology
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6 Sep 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Energy and Process Engineering Research Field Engineering » Process
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Uppsala University, Department of Information Technology Are you interested in probability theory, statistics, and mathematical modelling? Would you like to develop new methods for uncertainty
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industry partners to address one of the key challenges of the energy transition. As a doctoral student, you will receive excellent supervision, access to advanced research facilities, and opportunities