Hello everyone! I am applying for a research-based PhD and would like to know your thoughts about my motivation letter.
Any feedback is welcome (structure, where to improve what, what is not clear, etc).
P.S.: the letter is a bit lengthy, but so far this is what I get considering my previous experience.
Thank you in advance.
motivation letter
With this letter, I would like to show interest in the PhD position in __, publicly available by the ___ Group at the Institute __ of the __.
I am applying to this PhD position because I am interested in developing efficient models with stronger inductive biases that will shape the future of diagnostic tools and help in the diagnosis and treatment of cardiovascular patients. For that, I hope to contribute with my interdisciplinary background and experience in simulation sciences, advanced 3D data processing, and machine learning. I strongly believe that the opportunity to learn more about physics-informed machine learning, magnetic resonance, the cardiovascular system and its diseases will pave the way toward my objective of becoming a leading researcher in the field of enhanced health care.
My interest in simulation sciences began when I was working as an innovation engineer performing fluid analysis in a Brazilian startup. During the internship, I noticed that the simulation of real-world problems was computationally expensive and, looking forward to novel and efficient approaches, I decided to do a master's in Computational Sciences in Engineering at the __ University, Germany. There, I had the opportunity to improve my multidisciplinary abilities, and therefore I chose to specialize in the analysis of numerical methods, computational fluid dynamics, and machine learning.
During my master's studies, I sought to complement my theoretical studies with multidisciplinary research experience. Therefore, I collaborated with researchers of two institutes at the __ University. At the Institute __, I acquired experience in semantic segmentation, generative adversarial networks, autoencoders, and knowledge distillation techniques. This work gave me solid experience in PyTorch and TensorFlow. Furthermore, the strategies to compress knowledge and to detect domain mismatches in semantic segmentation neural networks proved to be useful techniques in my overarching goal of developing efficient algorithms for real-time inference.
At the same time, I also worked as a research assistant at the Institute __, where I further developed my skills in 3D simulations, meshing fitting, differential geometry, and advanced 3D data processing techniques for X-ray computed tomography data. These experiences gave me hands-on experience in 3D data acquisition and data processing with Matlab. More importantly, the multitude of data generated by X-ray equipment opened my eyes to the universe of possibilities to be explored, especially in face of physics-informed machine learning and geometric deep learning paradigms.
Capitalizing on my master thesis about advanced methods for understanding deep learning algorithms and relying on the cross-pollination of experiences in simulation sciences, data processing, and machine learning, I began to research more about methods to improve the sample efficiency of data-driven approaches. In particular, I started a collaborative work with researchers from the __University2, Germany, where I used PyTorch and TensorFlow to develop neural network approaches that combine simulation and experimental multi-fidelity data. Though still under development, the algorithms already demonstrate superior performance in comparison to previous simulation approaches, and can efficiently predict the stress-strain curve (behavior) and the mechanical properties of welded alloys.
In consonance with multi-fidelity neural networks, my PhD research goals lie in developing inference algorithms with stronger inductive biases and underlying advanced data synthesis models. In particular, I am excited about taking advantage of prior domain knowledge (e.g. laws of physics, symmetries, and invariances) to leverage the efficient application of machine learning algorithms in data-scarce but vital medical applications. I strongly believe that these techniques can help in providing appropriate guidance and personalization to the treatment of cardiovascular patients. In the long term, I am also excited about the growing expressive power of graph neural networks. Specifically, I believe that they will play an increasingly important role as they allow the modeling of pairwise interactions, thereby helping to identify, for example, correlations between cardiac micro- & mesostructures characteristics and cardiovascular diseases.
Considering the proposed research in personalized biophysical models with physics-informed machine learning, along with my previous multidisciplinary knowledge and experience, I am confident that this research will bring me one step closer to my goal of becoming a leading researcher in enhanced health care. Further, I strongly believe that the constellation of overlapping research interests will be a successful cooperation towards the development of highly efficient data synthesis and inference algorithms for enhanced health care of cardiovascular patients.
Thank you for considering my application. I am looking forward to hearing from you and your research team.