Hello. I would really appreciate it if you guys could suggest some improvements to the following letter. The goal is to persuade the admissions committee to award erasmus mundus scholarship :) Thanks a ton!
Dear Members of the Admission Committee,
The Erasmus Mundus Master Programme in Image Processing and Computer Vision (IPCVAI) stands out to me because it teaches computer vision as a full pipeline, mathematical imaging, modern deep learning, and trustworthy, sustainable AI, while moving across three research cultures. The structure is deliberate: PPKE (Budapest) builds fundamentals in visual processing, numerical analysis and ML; UAM (Madrid) deepens DL for vision and high-performance/green computing; UBx (Bordeaux) pushes advanced topics, explainable AI and domain applications. The fourth-semester internship embeds this knowledge in a lab or industry team. That arc mirrors how I want to grow: learn the theory well, stress-test it at scale, then apply it to real images (especially medical and safety-critical contexts). The consortium's triple degree and mobility aren't just attractive logistics, they are the setting I need to become a careful, research-minded engineer who can turn visual data into reliable decisions.
As per the journey I had with my undergrad, it gave me both the fundamentals and the motivation that drew me toward visual computation. Courses such as Data Structures and Digital Image Processing turned my curiosity into focus and then to be the future. By the sixth semester I was leading small projects, testing how algorithms learn structure inside data. That curiosity grew into research: I published two IEEE-published studies, and submitted a journal on Diabetic Retinopathy Grading using ESRGAN Super-Resolution. Through these, I gained hands-on experience with CNNs, image preprocessing, and evaluation of deep-learning models for medical imagery. Research taught me patience, precision, and the value of clear methodology. The IPCVAI curriculum's blend of mathematical modelling, sustainable AI and applied vision directly matches the skills I've built and the way I want to deepen them, translating algorithmic insight into meaningful visual intelligence.
My motivation to pursue this career has grown gradually, from fascination with how computers learn to solve problems collaborating how visual intelligence can improve everyday life. During my undergraduate years, I encountered both the creative and human sides of technology: solving abstract problems in class while seeing how timely information can make real-world difference. An incident when a friend from Nepal passed away from late-detected illness, we weren't best of friends but we were good friends in general, it quietly brought that awareness and encouraged me to steer my skills toward purposeful innovation. Image processing and computer vision, how simply pixels can make difference in our day to day life and we are unaware of it, it capture that blend of logic and empathy, systems that can interpret and support human decisions. I aim to deepen this work through a PhD in computer vision and AI, and later help strengthen Nepal's emerging research ecosystem by mentoring students and developing projects that translate global expertise into local impact.
Moving from Nepal to India for my B.Tech was more than a change of location it was my first lesson in adaptability. I arrived knowing almost no one, learned to navigate a new culture, and found confidence through collaboration. Those years showed me how much growth happens when you step outside what feels familiar. The IPCVAI consortium represents the next step of that same journey: three countries, three ways of thinking, and one connected goal. I am especially drawn to its structure because it reflects how I learn best by blending different systems and perspectives until something new emerges. The chance to study at PPKE, UAM, and UBx means learning how diverse teams solve the same visual problems through different lenses. I want to carry that mindset home to Nepal, where research communities are still forming, and help shape one that is as open, diverse, and curious as the Erasmus network itself.
When I look back, every major turn in my life from leaving Nepal to study in India to finding direction in computer vision started with uncertainty and ended in growth. The IPCVAI programme feels like the next step in that same pattern. What excites me most is not only its academic strength but the people and environments I'll get to learn from along the way. I've always learned best by working with others, asking questions, and figuring things out through practice. I hope to bring that same curiosity and persistence into this programme, contribute wherever I can, and carry those lessons forward to create something meaningful back home.
Sincerely,
Prabhat Chaturvedi
@prabhat
Dear Members of the Admission Committee,
The Erasmus Mundus Master Programme in Image Processing and Computer Vision (IPCVAI) stands out to me because it teaches computer vision as a full pipeline, mathematical imaging, modern deep learning, and trustworthy, sustainable AI, while moving across three research cultures. The structure is deliberate: PPKE (Budapest) builds fundamentals in visual processing, numerical analysis and ML; UAM (Madrid) deepens DL for vision and high-performance/green computing; UBx (Bordeaux) pushes advanced topics, explainable AI and domain applications. The fourth-semester internship embeds this knowledge in a lab or industry team. That arc mirrors how I want to grow: learn the theory well, stress-test it at scale, then apply it to real images (especially medical and safety-critical contexts). The consortium's triple degree and mobility aren't just attractive logistics, they are the setting I need to become a careful, research-minded engineer who can turn visual data into reliable decisions.
As per the journey I had with my undergrad, it gave me both the fundamentals and the motivation that drew me toward visual computation. Courses such as Data Structures and Digital Image Processing turned my curiosity into focus and then to be the future. By the sixth semester I was leading small projects, testing how algorithms learn structure inside data. That curiosity grew into research: I published two IEEE-published studies, and submitted a journal on Diabetic Retinopathy Grading using ESRGAN Super-Resolution. Through these, I gained hands-on experience with CNNs, image preprocessing, and evaluation of deep-learning models for medical imagery. Research taught me patience, precision, and the value of clear methodology. The IPCVAI curriculum's blend of mathematical modelling, sustainable AI and applied vision directly matches the skills I've built and the way I want to deepen them, translating algorithmic insight into meaningful visual intelligence.
My motivation to pursue this career has grown gradually, from fascination with how computers learn to solve problems collaborating how visual intelligence can improve everyday life. During my undergraduate years, I encountered both the creative and human sides of technology: solving abstract problems in class while seeing how timely information can make real-world difference. An incident when a friend from Nepal passed away from late-detected illness, we weren't best of friends but we were good friends in general, it quietly brought that awareness and encouraged me to steer my skills toward purposeful innovation. Image processing and computer vision, how simply pixels can make difference in our day to day life and we are unaware of it, it capture that blend of logic and empathy, systems that can interpret and support human decisions. I aim to deepen this work through a PhD in computer vision and AI, and later help strengthen Nepal's emerging research ecosystem by mentoring students and developing projects that translate global expertise into local impact.
Moving from Nepal to India for my B.Tech was more than a change of location it was my first lesson in adaptability. I arrived knowing almost no one, learned to navigate a new culture, and found confidence through collaboration. Those years showed me how much growth happens when you step outside what feels familiar. The IPCVAI consortium represents the next step of that same journey: three countries, three ways of thinking, and one connected goal. I am especially drawn to its structure because it reflects how I learn best by blending different systems and perspectives until something new emerges. The chance to study at PPKE, UAM, and UBx means learning how diverse teams solve the same visual problems through different lenses. I want to carry that mindset home to Nepal, where research communities are still forming, and help shape one that is as open, diverse, and curious as the Erasmus network itself.
When I look back, every major turn in my life from leaving Nepal to study in India to finding direction in computer vision started with uncertainty and ended in growth. The IPCVAI programme feels like the next step in that same pattern. What excites me most is not only its academic strength but the people and environments I'll get to learn from along the way. I've always learned best by working with others, asking questions, and figuring things out through practice. I hope to bring that same curiosity and persistence into this programme, contribute wherever I can, and carry those lessons forward to create something meaningful back home.
Sincerely,
Prabhat Chaturvedi
@prabhat
