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Field
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in
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to PhD level in a technical subject, with relevant experience: Expertise in machine learning for audio, including speech, music and ambient sound generation Research publications in relevant areas, i.e
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PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or PhD or equivalent degree in population/public health and other related medical fields with AI and ML
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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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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of alumnus Dev Joneja, PhD ‘89. Maxwell Fellows provide a bridge to enhance collaboration among faculty developing cutting edge tools in data science and in biomedical research, potentially spanning
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, funded by a Leverhulme Trust Research Leadership Award held by Dr Alessio Spurio Mancini. ECLIPSE's goal is to develop next-generation inference frameworks that combine machine learning with rigorous
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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execution and milestone completion. Job Requirements Strong background in AI/NLP or speech technologies, with experience in designing and implementing machine learning models. Proficient in software
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised