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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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skills in statistical analysis and mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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biomaterial research. The candidate will join a highly active international research group with PhD students and postdocs at BIOMAT. The candidate is expected to participate, often leading to valuable learning
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. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology for analysis of complex polygenic traits and prediction tools for precision
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active international research group with PhD students and postdocs at BIOMAT. The candidate is expected to participate, often leading to valuable learning experiences and co-authorships. The extent and
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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at the Faculty with a view to obtaining the degree of PhD. The successful candidate is expected to join the existing research environment/network of the department and contribute to its development. Read more
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in position as Researcher (position code 1108, or 1109 if the candidate has a PhD degree) in salary range NOK from 550 800 - 650 000, depending on competence and experience. From the salary, 2 percent
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understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to