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qualifications will count in the assessment of the applicants: Molecular Biology methods, Bioinformatics, Immunofluorescence, Flow Cytometry, Immunology, Fish Biology, Histology methods, Statistics Previous
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fields is required by the applicants: Molecular dynamics Software development Git Machine learning HPC Statistical mechanics/thermodynamics Personal skills: Excellent teamwork capabilities, social skills
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disciplines (data science, statistics, computational biology) to complement the Department’s research activities. An especially important task will be to facilitate integration of Artificial intelligence (AI
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groundwater data and statistical/physically-based modelling background to join our team. The postdoc will analyse groundwater level observations and related climate and site-specific data to assess
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machine learning techniques Interest or experience in statistical modelling and analysis Interest in mathematics and natural science Experience with scientific publication and conducting research Experience
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microscopy will be a strong advantage. Experience in the following will also count in the assessment of the applicants: chemogenetics, optogenetics, computational and statistical methods for analysis, rodent
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video, or click here to open video) Job description This Ph.D. position is focused on machine learning in realistic settings referring to statistical and system characteristics such as reliability and
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at the University of Oslo. The Department is engaged in teaching and research covering a wide spectrum of subjects within mathematics, mechanics and statistics. The research is on theory, methods and applications
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machine learning techniques Interest or experience in statistical modelling and analysis Interest in mathematics and natural science Experience with scientific publication and conducting research Experience
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defence are eligible for appointment. Demonstrated experience in epidemiological modelling and infectious disease dynamics Experience working in interdisciplinary teams Proven skills in statistical analysis