Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
-
manufacturing. As steel is highly recyclable its usage helps in creating a more sustainable world. For simulations of industrial processes concerning such complex materials one must typically rely on continuum
-
% of full time E13. The position is associated with the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by
-
the department of Experimental Psychology at the Faculty of Behavioural and Social Sciences. The candidate will work under the shared supervision of Prof Dr Jelmer Borst (Artificial Intelligence) and Prof Dr Elkan
-
should have strong digital signal processing and mathematical backgrounds evidenced by grades and/or prior publications. Additionally, the candidate should have expertise or strong interest (evidenced by
-
systems”, coordinated by Prof. Dr. Marco Salvalaglio and Prof. Dr. Axel Voigt and funded by the German Research Foundation (DFG). The core activities will focus on the investigation of disordered correlated
-
-matter Bose-Einstein condensates (BEC) using optical signals in the telecom and infrared (IR) spectral ranges. Project background: The control methods are enabled by strong exciton-photon and exciton
-
, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
-
and Cell Identity, relevant for stem cell biology and cancer. The position will be within the Molecular Signaling and Bioenergetics group headed by Prof. Mathias Ziegler. About the project/work tasks
-
Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather