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of Applied Mathematics: Statistics Position You will work actively on the preparation of a PhD thesis in the field of statistics and machine learning. The envisioned topic is practical challenges of causal
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high 2:1 in computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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with Robotics Excellent programmer in Java / C / Python / ROS or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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and understanding of complex biological systems and biodiversity. You will get the opportunity to learn about both simple and complex biological models, computer programming, data visualisation, and
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microkinetic modelling or/and machine learning, especially in the area of polymer design, is valued. Experience with modelling macromolecular structures or/and predicting polymer material properties is highly
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/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning. The successful
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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The accuracy and precision of opto-electronic 3D motion capture systems created a global shift towards lab-based biomechanical analysis over the last 30 years. With the advent of AI and machine