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. There will be close collaboration with policymakers to apply empirical safety research for AI regulation and governance. You should possess a PhD or DPhil (or near completion of) in Machine Learning. You
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across the broader TU Delft research community. Job requirements PhD in computer science, AI, machine learning, data science, network science, computational social science, or a related field. Expertise in
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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demonstrated through at least one first-author publication: machine learning/computer science, computational biology/bioinformatics, cheminformatics. Individuals should have or be expected to have a PhD with
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interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present
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to learn about, (road) network traffic flow theory and simulation. You are interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding
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providing industry actors with practical evaluation tools, such as technical testing platforms for AI. Your profile PhD in Computer Science, with a thesis related to machine learning, software engineering
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, such as technical testing platforms for AI. Your profile PhD in Computer Science, with a thesis related to machine learning, software engineering, software security or a related area Strong programming and
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statistical / machine learning methods, pathway/network analysis or artificial intelligence approaches. Other requirements: The candidate should have a PhD or equivalent degree in bioinformatics, artificial
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demonstrate exceptional skills and experience relevant to the role. Applications are invited for the post of Postdoctoral Research Associate in Applied Machine Learning for High-Stakes Regulated Domains in