Rate my motivation letter

I am applying for PhD in university of Copenhagen, here is my motivation letter, I'd appreciate if you can give me you opinion specially if you are someone who recruits students

Dear Dr. (Professor's name)

I am applying for the PhD fellowship in simulation-supervised machine learning for biology within the DREAM project. I hold an MSc in Software Engineering and have developed a strong research and technical background in machine learning and computational systems. I am particularly motivated by a fundamental challenge in biological imaging: even powerful learning methods remain constrained when reliable, task-specific labels are scarce or expensive to obtain. In microscopy, acquiring sufficiently diverse experimental data is challenging, while annotating the underlying biological structures can be even more demanding. Conventional augmentation can increase the diversity of existing observations, but it cannot fully capture the biological and experimental variability of real data. Simulation-supervised learning offers a compelling alternative: biophysical simulations can generate structures with known ground truth, while rendering or signal-generation models can translate them into synthetic observations. The key challenge, however, is not simply to produce realistic-looking images, but to reduce the simulation-to-reality gap sufficiently for models trained on synthetic data to generalize to real experiments. I am particularly interested in how real, unlabelled measurements can be used to calibrate simulations and rendering models toward this goal.

I have experienced this problem from two different angles. On DePerio, with BioSA Lab at York University, the training set for microscopic images was limited; I trained and fine-tuned multiple models to detect oPMNs and deployed the final model in an end-to-end application for dental clinics. This experience made the prospect of building differentiable rendering and simulation systems that preserve known ground truth while matching real microscopy particularly compelling to me. In my URLLC/eMBB graph neural network work, I also faced limitations in the availability of labelled training data, which led me to formulate a differentiable objective based on inverse bandwidth and variance rather than relying on a conventional supervised loss. Although the application is very different, this experience strengthened my interest in differentiable optimization and in learning systems where the objective can encode structure beyond manually provided labels. These experiences have motivated me to go further into differentiable simulation and rendering, distribution matching, and domain adaptation for sim-to-real problems.

My background in software engineering has given me strong experience in developing machine-learning systems and implementing computational methods, which I now want to apply to differentiable simulation and scientific computing. In this PhD, I hope to develop research systems in which differentiable simulations and rendering models can be calibrated against real, unlabelled biological measurements, making learning from synthetic ground truth practical for real experimental data. Chromatin organisation is an application I would particularly like to explore, especially the possibility of using simulation-supervised methods to infer biologically meaningful structure from limited and noisy microscopy data. More broadly, I am interested in the methodological challenge underlying this application: how computational models of biological systems can be made sufficiently faithful to experimental data that they become useful sources of supervision themselves. This combination of machine learning, differentiable modelling, and biological simulation is what makes this PhD particularly well aligned with the direction I want to take as a researcher.

Sincerely,

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