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the possibilities and limitations of the use of CL in semiconductor metrology and to develop practical workflows for CL tomography. The project will be carried out in close collaboration with
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is highly committed to an inclusive and diverse work environment: we want to develop talent and creativity by bringing together people from different backgrounds and cultures. We recruit and select on
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an open and collaborative environment in which we focus on hands-on learning and personal growth of all group members. We are looking for excited and talented students, who are eager to develop new skills
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their performance (using e.g., automated code generation, train-of-thought prompting, etc.). We will use our GPU cluster to fine-tune the LLM using reinforcement learning, so the system continuously learns and
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carbon economy remain largely unknown. In this project, you will develop data analysis tools to extract transport statsitics from flow videos acquired in a newly built multi-scale fungal imaging setup