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Field
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requirements are Experience in working with large-scale spatial-temporal traffic and/or travel behavior data, e.g., loop detector, floating car data, GPS data, cellphone data. Experience with transport
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learning paradigms as well as interactive data- and model exploration with domain knowledge towards optimal performance in real-world generalization scenarios. AqQua is a large-scale collaborative research
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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Your Job: • Addressing socio-technical research questions in the context of the transformation of the German energy system • Analyzing microdata (e.g., survey data, digital behavioral data) and
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Performing atom probe tomography Application of deep learning algorithms for the correlation of the obtained experimental data and their analysis The majority of the work will be undertaken in Hamburg Your
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, as well as deploying and retrieving equipment. Once the large, multichannel seismic data sets have been collected, you will take care of the complex data processing and interpretation together
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public debates and information/service – we contribute to maintaining and increasing sustainable economic prosperity and social participation under constantly changing conditions. Do you want to know more
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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. Furthermore, we develop advanced scattering methods and machine learning tools for data analysis. For more information, see www.soft-matter.uni-tuebingen.de Qualification and skills Candidates should have good
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
ecosystems. Your Profile A PhD in marine biology, conservation biology, fishery management & conservation, or related fields A strong background in handling large data sets, programming (preferably in R