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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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engineers through PhD and postdoctoral supervision. Key responsibilities include: Establishing and leading an internationally recognised research programme in computer engineering in one or more of: (i
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have an active scholarship program. Faculty may be required to teach online and nonstandard hours. Salary and benefits are competitive. About the Department: The Department of Computer and Information
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background in geomodelling, geophysical and geotechnical investigation, geomechanical engineering, and machine learning. You will be expected to work effectively on a geophysical/geomechanical project, to
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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warmer and wetter world. RAPTURE brings together high resolution physical simulations, machine-learning climate emulators, and new perspectives on the dynamics of weather and climate to understand i) what
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education, an ability and willingness to teach at all levels of the program, and a PhD in operations research, mathematics, statistics, or a related field by the start of the appointment. Salary will be
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://isor.univie.ac.at/ ) of the University of Vienna is offering a PhD position in the area of Mathematical Statistics and Machine Learning, starting on 01 March 2027. The group's research is focused on the current
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Data Analytics, including but not limited to: “Data Analytics for Social Research”, “Applied Analytical Statistics for Social Scientists”, “Machine Learning with Social Data” and “Social Networks