Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
position within a Research Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Scientific / postdoctoral posts Job description: Your Job: In our
-
Your Job: In our team you will collaboratively work on: Collaborative development of semantic artifacts in materials science, in a scale- and method-bridging approach for crystallographic defects
-
Postdoc – Synthesis and up-scaling of high-performance active materials for Na solid-state batteries
manufacturing process of future battery systems - from materials synthesis to characterization. Your Profile: Successfully completed scientific university degree (Master) in the field of chemistry, materials
-
to the FAIR-ification of data, establishing systematic data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s
-
: Very good performance in your Master`s degree in the fields of energy technology, mechanical engineering, materials science, civil engineering, industrial engineering, physics, resource management
-
data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s degree in Engineering, Computer Science, Physics
-
machine learning-based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration
-
to accelerate materials research and develop the next GW photovoltaic technology. We use automated research equipment to experimentally produce, characterize and optimize solar cells. In doing so, we collect a
-
Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: You will strengthen the data science and machine learning
-
information from experiment data Evaluation and development of specialized and/or interpretable machine learning approaches for the domain of materials science, physics, microscopy Incorporation of machine