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: Preference Learning for LLMs Apply for this job See advertisement About the position Integreat – the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo – invites applications
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context. • Conduct statistical analyses, longitudinal modelling, or machine learning approaches as appropriate. • Develop documentation, codebooks, or tools to support reproducible research. • Lead
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
and orchestration technologies for real-world logistics and decision support. Collaborate with leading experts in Artificial Intelligence and Machine Learning at ANU and Defence stakeholders. About the
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research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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candidates will have specialist knowledge in signal processing and algorithm design, with experience in machine learning, AI system development and reinforcement learning along with a strong publication record
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team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics/. What you’ll
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for additional teaching. The Department of Religious Studies has five full-time faculty and a history of innovative teaching and the promotion of independent learning. The Department's website is http
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your machine learning expertise to cutting-edge automated optimisation challenges? We are seeking a Research Fellow in Digital Chemistry and Engineering to combine self-optimising flow reactor technology
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status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment