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In today’s rapidly evolving world, it has become increasingly important to delve into the multifaceted nature of computational biology. As an aspiring researcher, I am passionate about leveraging cutting-edge technology to unlock the secrets of life.
I spent the summer of 2024 trying to model how a single mutation in the FOXP2 gene changes a zebra finch’s song. It didn’t work. But the failure taught me what I want to do next: build computational tools that biologists can actually trust.
In this study, we have investigated the effects of FOXP2 mutations on song production in Taeniopygia guttata, leveraging a comprehensive computational pipeline to facilitate downstream analysis. Our findings demonstrate that there exists a statistically significant relationship between the mutation status and song variability (p < 0.05) as previously suggested by Smith et al. (2019).
We examined how FOXP2 mutations alter song production in Taeniopygia guttata. Mutation status was associated with increased song variability (p < 0.05), consistent with Smith et al. (2019).
I would be an excellent fit for your prestigious lab due to my strong background in machine learning and computational neuroscience. My research interests align perfectly with your work on neural representations.
Your 2024 paper on disentangled visual representations is the closest match I’ve found to what I want to spend a postdoc on. I’ve been reproducing Figure 3 with the chair-image dataset for six weeks, it works, mostly, and I think I know why ‘mostly’.
If awarded the Marshall Scholarship, I would utilize this incredible opportunity to further my research and contribute to the global scientific community in meaningful ways.
With the Marshall, I’d spend year one at Edinburgh in the Storkey group working on uncertainty-aware ML for clinical trials, specifically on a project I’ve been corresponding with Dr. Storkey about since March. Year two, I’d move to the Crick in London to test the methodology on the lab’s ongoing oncology cohort.
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