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libraries like NCCL/RCCL and experience with high performance computing middleware is highly desirable. Optimizations of large parallel code bases and experience with GPU programming languages such as CUDA
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therapies, cell programming and repair, bioengineering, and computational health. Through this research, we build the foundations for medical innovation. Together with our partners, we seek to accelerate
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guidance from experienced pharmaceutical research experts and world leaders in the field of DNA Damage Repair. This work is funded through a 3-year £3M programme of work supported by the philanthropic Chris
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standard and novel models of cardiovascular inflammation, as well as rare human biospecimens and have most recently established a dedicated therapeutic mRNA program. All of these projects have a strong
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but able to take direction when necessary High degree of self-motivation Ability to multi-task and integrate the demands of different activities in parallel to maintaining operation of the laboratory
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown