73 algorithm-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" PhD positions in United Kingdom
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an adhesive heavy application, and to find methods for End-of-Life disassembly. Battery pack construction uses adhesives to fix multiple cells in place, creating a block of battery modules. The adhesive has
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end
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Dysregulation of the complement system is increasingly recognised as a key driver of neuroinflammation and neurodegeneration in progressive CNS disorders, including multiple sclerosis and epilepsy
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into the genomics of population structure and speciation in the Malawi cichlid genus Labeotropheus. This post will build on past work in which over 1000 samples of Labeotropheus from multiple species/populations have
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would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses from visual and auditory cortices recorded over multiple days Apply and
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consider the multiple health and other impacts when deciding on appropriate action. This PhD studentship will conduct an assessment of the impacts of policies aimed at improving home energy efficiency
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural