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advanced programming skills in a high-level computer language (Python or related) hands-on experience with instrumentation development excellent oral and written English skills collaborative mindset Skills
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computer language (Python or related) hands-on experience with instrumentation development excellent oral and written English skills collaborative mindset Skills and knowledge considered as assets: working
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methods Supervising a small team of PhD and MSc students Developing new foundations of augmentations for digital pathology Potentially developing multi-scale AI models for cancer patient stratification
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The German Cancer Research Center (DKFZ) is one of Europe’s largest cancer research centers. “Research for a life without cancer" is the mission of our world-class scientists and all our team
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natural and AI-designed protein domains, along with an engineered fusogenic protein. The technology has been recognized with the m4 Award, the GO-Bio next program, and is currently under revision in a top
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. Excellent communication and organizational skills and proficiency in English are mandatory. Proficiency in German is desirable. Application Process: Interested candidates should submit the following documents
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-latitudes (Kühlungsborn, Germany) and high latitudes (ALOMAR, Norway). The project AMILIS (Anthropogenic Material Influx Lidar Study) is funded by the German Aerospace Center. The position includes setting up
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the candidate’s expertise and research interests, providing room for creativity and scientific growth in a collaborative and dynamic environment. Applicants should hold/soon expect a PhD degree in area of the life
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to complement your academic profile by collaborating with leading scientists in the field and participating in joint research activities, lecture series and engaging in teaching and (PhD) supervision In addition
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for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background in image processing and analysis, including deep learning