Sort by
Refine Your Search
-
Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
-
-Learn data generated by NOAH partners; develop a generative ARCA component, exploring autoencoder- and/or diffusion-based approaches for generating ecologically plausible microbiome configurations; apply
-
design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
-
sediment management. This will be done for two case studies, namely Western Scheldt and Wadden Sea. To facilitate knowledge exchange and the learning process, interactive workshops will be organized
-
street-view imagery (SVIs), geospatial data, mobility data, and qualitative insights to generate evidence-based recommendations for policymakers, planners, and researchers to create healthier urban
-
different expertise, including chemists, biologists, bioinformaticians and technical staff. The research in this division focuses on the use of mass spectrometry based enabling technologies to deeply
-
different expertise, including chemists, biologists, bioinformaticians and technical staff. The research in this division focuses on the use of mass spectrometry based enabling technologies to deeply
-
and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems, analysing the complex
-
completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
-
scale. Besides PI’s Kraal and Wolthers, the PHOSFLUX team consists of a NIOZ-based PhD student performing experiments and analyses, and a UU-based postdoctoral researcher who focuses on modelling