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that are tailor-made to the research questions and data of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and
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of Computer Science and affiliated with the Information Systems and Human–Computer Interaction (ISCHI) research group. Your immediate leader will be the unit leader of the Information Systems and Human–Computer
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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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healthcare data. * A team player who thrives as a member of a highly functional cross-disciplinary team Preferred Elements * B.S, M.S., and/or PhD in Computer Science, Biomedical Informatics, Machine Learning
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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postgraduate qualification in Data Science / Computer Science (PhD preferred) Strong expertise in Python and/or R, SQL, data engineering and machine learning Experience with EMR systems (Cerner highly desirable
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Fusion Tribrid MS and Waters Q-ToF instruments are highly desired. Experience handling and analyzing large-scale MS, MS(MS) and/or proteomics-like datasets using statistical and machine learning techniques
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opportunity to contribute to leading-edge research at the intersection of applied machine learning and clinical dental practice. As a member of our team, you will help translate contemporary data science
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assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
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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