Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
genomic data using deep learning methods in order to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification
-
respect to the identification and functional annotation of genes involved in, for example, cell wall degradation, carbon flux etc. Screening metagenomic and genomic data using deep learning methods in order
-
, software developers and domain scientists on, e.g., Developing deep learning-assisted methods for in situ electron microscopy with enhanced time resolution using adaptive scanning Contributing
-
, and gas molecules, and it maximizes the active interfacial area between electrode and electrolyte phases. A deep understanding of the complex interplay between electrokinetic transport, surface charging
-
Science, or Theoretical/Computational Chemistry and a subsequent PhD Degree Profound programming skills in python and its ML and NN modules Extensive background in ML, NN and related deep learning methods
-
Chemistry and a subsequent PhD Degree Profound programming skills in python and its ML and NN modules Extensive background in ML, NN and related deep learning methods ideally with a focus on digital image
-
the application in satellite remote sensing, but with the perspective to apply the methods to different domains. In detail you will: Develop, implement, and refine ML techniques for self-supervised Deep
-
). The leap to practical application of metal-air batteries won’t happen without deep understanding of the degradation processes occurring at different conditions and modes of operation. The failure