VUB
Master's Applied Computer Science in Digital Health, Computer Science
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I bring a strong foundation in data integrity and machine learning development, having engineered pipelines utilizing classic ML frameworks and neural networks. Through this hands-on technical work, I have extensive experience working with structured and unstructured datasets, evaluating how data quality directly impacts model convergence and inference. I am highly proficient in utilizing Python (pandas, NumPy, scikit-learn) to inspect datasets, verify annotations, and correct biases or errors within data inputs. My core qualification is the combination of deep technical literacy with strict attention to detail. I understand the mathematical and logical principles underpinning modern AI models, allowing me to rigorously audit training datasets, evaluate generative outputs for factual accuracy, and ensure high-fidelity data labeling that meets strict engineering requirements.
Master's Applied Computer Science in Digital Health, Computer Science
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