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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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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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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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-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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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
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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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research profile to further integrating wet-lab techniques (such as single-cell sequencing, -omics) with advanced data analysis, for example through bioinformatics, machine learning, or AI. Themes such as
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cybersecurity expertise with modern AI techniques such as machine learning, deep learning, or large language models? Then we strongly encourage you to apply. You will join an established team with 25+ members