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and motivated individual to pursue a PhD in the area of machine learning with focus on explainable clustering. The prospect PhD student will join a research team in KTH led by Professor Aristides Gionis
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | 22 days ago
program at The Hospital for Sick Children, University of Toronto, and the Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and
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collaborative mindset and excellent communication skills in English. Significant Advantage: Previous experience with adversarial machine learning, offensive security, or publications in top-tier conferences (e.g
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 5 days ago
: rigorous, organized, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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applications for three tenure-track faculty positions in the area of Artificial Intelligence (AI) and Machine Learning (ML) in Drug Discovery. This is a unique cluster hire initiative spanning the College
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position is on a 9-month service period with an anticipated start date of Sept 16, 2025. This position is aimed at recruiting an exceptional candidate in the field of physics-based Machine Learning
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organ-on-a-chip (OOC) models, colony picking and bioprinting). The ideal candidate should have strong expertise performing machine learning (ML), computational biology with the capability and/or
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manipulation techniques for soft objects based on CNNs. Key Responsibilities: Modelling of soft objects. Development of robot vision system for detection and localization of soft objects using machine learning