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Python or R A willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international level
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combine EMI footprints, which capture normal variations through characteristic curves and statistical distributions, with state-of-the-art machine learning and deep learning techniques (e.g., one-class
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of the electromechanical aspects of industrial machines, with an emphasis on Industry 4.0 technologies such as machine vision, AI or digital twins. A digital twin can be defined as a virtual replica of a physical system
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support tasks for the Bittremieux Lab, such as assisting in practical teaching sessions and supervising Bachelor and Master students. Profile You hold a Master degree in Computer Science, Machine Learning
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machine learning, and exploring causal inference in complex biological systems. As a member of our team, the successful candidate will have the opportunity to work closely with experts in the field and
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-type specific samples, state-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches
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(involving, e.g., computer programmers and data scientists, military startups, technology firms) and how it leads or has led to new and compounding forms of civilian harm in Ukraine and Gaza. It is co-led by
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, mathematics or a related domain. You have a solid academic track record, at least at the cum laude level. You are interested in both Machine Learning and Symbolic/Logic-based AI methods. You strive
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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covariate adjustment of multivariate outcomes in clinical trials, causal machine learning, exponential random graph models for modeling mpox, demography and infectious disease epidemiology. You will