Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- DAAD
- Academic Europe
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics
- Forschungszentrum Jülich
- Heidelberg University
- Karlsruher Institut für Technologie (KIT)
- Paderborn University
- University of Kassel •
- University of Oldenburg
- University of Potsdam •
- University of Tübingen
- 3 more »
- « less
-
Field
-
for educational materials. The successful candidate will join the project “Instant Instructor Insights (I³): AI Feedback for Effective and Engaging Digital Learning Materials” within the Motivation Science Lab
-
installations such as a deep geological repository for nuclear waste is both a strategic necessity and a challenge. The Department of Actinide Thermodynamics of the Institute of Resource Ecology is looking for a
-
and interpretable quality assessment algorithms. This research combines various machine learning topics, including uncertainty, explainability, and fairness in supervised and unsupervised deep learning
-
learning and explainability of deep neural networks (XAI) are being developed, particularly for use in biomedical applications. Further information about the department can be found at https://uol.de/en
-
2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
-
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
satellite imagery with significantly higher spatial resolutions, thereby improving subsequent monitoring analyses using the spatially-spectrally exceptional PACE data. Then, an Integrated Deep Learning
-
, formatting and visualizing/interacting with big acoustic and satellite datasets. Collaborate with AI specialists to train and validate deep-learning models for biodiversity classification. Participate in field
-
, including transient absorption and time-resolved fluorescence Application of 2D Electronic Spectroscopy to selected molecules Development, training, and application of the Deep Neural Network (DNN
-
of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation
-
models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in