75 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" research jobs at Nature Careers in Denmark
Sort by
Refine Your Search
-
: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
-
5-year undergraduate nanotechnology programme and nanoscience graduate programme (https://phd.nat.au.dk/programmes/nanoscience/) the center provides a full educational environment. In
-
application of magnetically enhanced electrocatalysis for water splitting and CO2 reduction (see e.g. https://doi.org/10.1038/s41560-019-0404-4). Your main tasks may include Application of new materials and
-
personality. The Department of Biology The Department of Biology (http://bio.au.dk/ ) provides a framework for research and teaching in all major biological subdisciplines. The department is especially known
-
Applications are invited for a postdoctoral position in the group of Dr Aleksandr Gavrin ( https://mbg.au.dk/a-gavrin/ ) at the Department of Molecular Biology and Genetics, Aarhus University
-
will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
-
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
-
analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
-
key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
-
biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be