Salar Sadek

Background

About Salar Sadek

I’m an engineer with 10+ years of experience in durability and fatigue, including CAE/FEA and engineering analysis, and I hold a PhD in fatigue design. My strength is turning complex physical systems into models you can trust: validating assumptions, working with real-world data/results, and translating technical findings into clear guidance for stakeholders.I’m currently completing an MSc in Data Science & Machine Learning, and I’m now shifting my main focus toward Data Science / Machine Learning Engineering—especially statistical modeling, time-series & sensor data, and ML/DL prototyping. I enjoy building end-to-end solutions: problem framing, data preparation, feature engineering, model development, and rigorous evaluation (metrics, thresholds, and uncertainty).Recent project: early prediction of sepsis from ICU patient time-series using LSTM/GRU/Transformer models in TensorFlow/Keras, evaluated with PR-AUC for class imbalance, built as a reproducible pipeline from preprocessing to evaluation.I’m particularly interested in roles and projects involving digital twins, surrogate models, physics-informed / simulation-to-ML workflows, predictive modeling, reliability/quality analytics, and applied deep learning. I’m open to opportunities as a Data Scientist, Applied Data Scientist, or Machine Learning Engineer, especially where engineering domain knowledge and robust validation make a real difference.Teaching experience: Vehicle Engineering (basic), Solid Mechanics (basic), Fatigue, Reliability & Design (advanced; course development).Specialties & tools: Fatigue design, Reliability engineering, CAE/FEA, Python, Git, Statistics, Machine Learning, Deep Learning, scikit-learn, TensorFlow, Keras, MATLAB, ANSYS, Abaqus, HyperMesh, Simlab.