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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be
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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science,is looking for a: PhD Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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interest in neuro-behavioral sciences and a passion for behavioral signals. Demonstrable experience in advanced data analysis and data collection. Familiarity with machine learning and proficiency in Python
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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on what we learn from organisations where cybersecurity is managed successfully. The main research question the team will answer is: “What do successful organisations do differently on the individual, team
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their ways of working based on what we learn from organisations where cybersecurity is managed successfully. The main research question the team will answer is: “What do successful organisations do differently
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cybersecurity solutions that organisations can implement in their ways of working based on what we learn from organisations where cybersecurity is managed successfully. The main research question the team will
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solutions that organisations can implement in their ways of working based on what we learn from organisations where cybersecurity is managed successfully. The main research question the team will answer is
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networks, or network science, and relevant background knowledge n methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples