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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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University's Faculty of Arts and Sciences to study spatial and temporal aspects of interaction patterns in biological systems. We are looking for exceptional candidates with background in machine learning and
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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challenges facing the nation. We are seeking a full-time Distinguished Scientist, Artificial Intelligence and Machine Learning, who will support the Cyber Resilience and Intelligence Division (CRID) in
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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Sessional Lecturer, INF2205H - Designing Sustainable & Resilient Machine Learning Systems with MLOps
University of Toronto Faculty of Information Sessional Lecturer Winter Term 2026 (January - April) INF2205H – Designing Sustainable and Resilient Machine Learning Systems with MLOps Course
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/ Publish in prestigious journals Apply now to drive innovation in machine learning and spatial data management at a world-class institution Investing in you - benefits package including salary packaging
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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data