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Field
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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WDM switches and the fast control to enable novel low latency highly scalable and flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become
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departments of this type in the country, with a particular strength in power engineering. The increasing use of liquid cooling in aerospace electrical machines introduces direct interaction between liquid
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at Saxion University of Applied Sciences, you will contribute to the development of machine-learning models that connect powder characteristics and process parameters with the properties of the final
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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. Familiarity with automotive systems, electric vehicles, and embedded BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use