Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Technical University of Munich
- Fraunhofer-Gesellschaft
- Forschungszentrum Jülich
- Nature Careers
- DAAD
- Leibniz
- ;
- Free University of Berlin
- Deutsches Elektronen-Synchrotron DESY •
- Helmholtz-Zentrum Geesthacht
- Max Planck Institute for Molecular Genetics •
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Max Planck Institutes
- RWTH Aachen University
- Saarland University •
- Technische Universität Braunschweig
- Technische Universität München
- University of Bremen •
- University of Tübingen
- 10 more »
- « less
-
Field
-
software frameworks Development of new signal processing algorithms (PHY/MAC) in conjunction with software-defined radio hardware Development and validation of AI/ML methods for mobile communications systems
-
and simulation aspects across a wide range of fields - from biomechanics and geophysics to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dimensional problems
-
Learning Algorithm for Grid Optimization linked to Bayesian uncertainty outputs Test bidirectional interaction: Bayesian updates → Reinforcement Learning policy adaptation → grid performance feedback
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
-
-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome and transcriptome data) by using efficient algorithms and
-
concise code Diligence in implementing, testing and documenting algorithms and results Curiosity about a deeper understanding of Deep Learning architectures What you can expect Fascinating challenges in a
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we