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history analysis and possibly also with machine learning. Another specific example is statistical software development, e.g. implementing tools and algorithms for working with causal discovery
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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Computational Linguistics, Computer Science, or a related field (e.g., Cognitive Science), with specialization in NLP, Machine Learning, or a similar area. Experience and interest in leading largescale open
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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are looking for a candidate with a track record within observational research and with experience with machine learning or other relevant data science techniques. The scientific background
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
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at CPPEM (see more below). We are looking for a candidate with a track record within observational research and with experience with machine learning or other relevant data science techniques. The scientific
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient