Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- Technical University of Munich
- Nature Careers
- University of Greifswald
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Free University of Berlin
- Helmholtz-Zentrum Hereon
- Leibniz
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
- Max Planck Institute for Intelligent Systems, Tübingen, Tübingen
- Max Planck Institute for Radio Astronomy, Bonn
- Max Planck Institute of Biochemistry, Martinsried
- 2 more »
- « less
-
Field
-
/d). Project overview Protein design is a rapidly evolving field, with machine learning taking an increasingly important role in the development of novel protein biotechnologies. Yet, computer
-
Experience with modern molecular biological and biochemical methods (immunohistochemistry, RNAScope etc.), prior experience with behavioral tests/surgical procedures and analysis (i. e. DeepLabCut, Boris
-
measurements as well as highest spatial and density resolution. Additionally, the highly coherent beam enables the application of phase contrast methods with a spatial resolution below 50 nm. Join the Cluster
-
standard software used for FRB or pulsar searches and analysis Practical experience in applying Machine Learning methods to data Experience with computer clusters and HPC Willingness to supervise Masters/PhD
-
of GHG exchange using the canopy method with surveys of additional data (partly also from cooperation partners), such as water levels, soil moisture, soil and air temperatures, DOC and nutrients in pore
-
twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
-
for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain
-
the application of AI methods in engineering. Description: Nowadays, computer-aided manufacturing (CAM) methods are used to a large extent for the production of complex machine components, in which NC
-
, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis