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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about advancing Machine Learning by integrating
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contribute to the development of innovative, physiology/ machine learning-driven clinical solutions and decision support tools for critically ill patients, focusing on cardiovascular and respiratory monitoring
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of the following subjects: scalable data management, systems for machine learning, distributed and parallel systems, or cloud-based systems. We are especially interested in researchers who build working systems and
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to overall technology strategy and help shape the company’s long-term product direction. Job requirements Background MSc/PhD in Electrical Engineering, Computer Engineering, or Computer Science (or equivalent
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within the field of machine learning, search, and reasoning techniques. Demonstrable knowledge and/or experience in algorithms and programming is a must; Is able to translate and convert this knowledge
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causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via consultations and collaborative research, train researchers through workshops, and mentor
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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. We are particularly interested in candidates who are comfortable engaging with senior professionals and who combine commercial acumen with a genuine interest in leadership development and learning
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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researchers to design and develop a wide range of innovative projects, for example involving causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via