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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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are expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The
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expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The Department
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has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct
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. Observational experience/expertise to compare computational results with data from (sub)mm observations, especially ALMA, are highly appreciated. Expertise in applying machine-learning techniques is an additional
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deliverables with high standards of accuracy and clarity. Teach and supervise BSc and MSc student projects, and be co-supervisor for PhD students We are looking for candidates with: Skills in molecular biology
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QGG Aarhus University seeks two Postdoctoral researchers in Quantitative Genetics of sustainable ...
tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise
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sciences Strong background in deep learning, with experience in probabilistic models (e.g., Variational Autoencoders, Bayesian approaches) Proficient Python programming for machine learning and scientific
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and engineering optical setups Experience with coherent control of quantum systems Competence in electronics design and hardware control Ability to acquire and process large datasets Enthusiasm
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and maintenance of monitoring buoys and related sensor systems. Apply image analysis and machine learning techniques to ecological datasets. Develop and implement multi-platform monitoring frameworks