11 evolutionary-algorithm-"DIFFER" PhD positions at University of Cambridge in United Kingdom
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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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) 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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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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international student would need to self-fund the difference between the home and international fees. For further information click the 'Apply' button above.
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Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties will explore the range of possible non-sulphate aerosols - mostly powdered ceramics with different coatings - and will consider how
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Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties will be to first explore the range of possible non-sulphate aerosols - mostly powdered ceramics with different coatings to allow
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-based reviews, data collection and analysis, written outputs, and the dissemination of research findings to different audiences, including through investor briefings and academic publications. We
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PhD students, three Research Assistants, as well as research visitors and interns. We value our team's complementary skills (e.g., differing backgrounds, research approaches, and areas of expertise
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enthusiastic to join our diverse and interdisciplinary team. Solid communication skills are required to interact with group members and other researchers within the RESYDE project and at SLCU with different
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the proportion of individuals within different ethnic groups classified as high risk. - Develop multistate survival models (MSM) to estimate transition parameters between cancer progression states across risk