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and implementing vision processing algorithms that enable robust robot tracking and autonomy. The ideal candidate will possess hands-on experience designing, implementing, and deploying computer vision
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
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developments such as novel algorithms to support logistics operations, novel automation approaches or the design and development of new digital support tools for logistics providers. Significant flexibility
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pivotal role in analysing large-scale genomic and clinical data, applying cutting-edge algorithms, and collaborating with renowned experts in the field. At PBCI, we believe in fostering a collaborative and
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for the Future of Intelligence (CFI). The candidate will contribute to teaching and lead a research programme in one or more of the following areas: ethical use of machine learning, AI safety, algorithmic
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(Biochemistry, Neuroscience) MSc degree (or equivalent) in Epidemiology, Biostatistics, Data Science or related discipline. Strong quantitative analysis skills and experience developing algorithms and/or
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on juggling multiple projects. Confident communicator who can explain complex ideas simply and build trust with academics, entrepreneurs and industry. Interest in intellectual property and software licensing
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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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that are required are the ability to work on multiple projects simultaneously including coordinating with other group members and collaborators. Expertise in living crystallization-driven self-assembly of conjugated