468 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions in Belgium
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should hold a Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, Data Science, or a closely related field Solid background in machine learning and natural
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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machine. We develop quasi-Newton coupling algorithms for partitioned simulation of FSI, and we solve challenging FSI problems in the energy transition and in industry. This research is often in
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that monitors the radiation beam in real time, learns from data how the dose evolves, and autonomously reacts to protect patients from anomalies. The work involves both experimental physics and AI modelling
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. More information can be found on our website: https://www.b-phot.org . The candidate will be supervised by Prof. Francis Berghmans, and co-supervised by Dr. Sidney Goossens and Dr. Sergei Mikhailov
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Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM) at UCLouvain. LIDAM brings together researchers in economics, econometrics, statistics, and operations research, and offers
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The position will be hosted at the Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM) at UCLouvain. LIDAM brings together researchers in economics, econometrics, statistics, and
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work includes https://doi.org/10.1101/2025.0... and https://doi.org/10.64898/2025.... Profile You have: A PhD in structural biology, cell biology, biophysics, bioengineering, or a related
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Software Testing and Analysis Machine Learning and Large Language Models Web Systems and IoT Systems The candidate must possess strong programming skills in Java or Python and is willing to learn all
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to that present in the LMC is recommended, e.g. on AI/Machine learning in drug design, assay development, bioconjugate chemistry, fragment-based discovery, DNA-Encoded Libraries (DEL), sustainability aspects, etc