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Field
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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems
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ensure intellectual and international diversity. By cooperating with other university faculties and departments, we foster broad professional and academic development and prepare our students
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usable in continuum-scale soil and plant-water models Contribute to the development and documentation of reusable, open, and reproducible molecular simulation workflows Collaborate with researchers working
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: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https
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Joint degree / double degree programme No Description/content Our aim is to train a new generation of biomedical scientists who can take on the diverse challenges of making fundamental scientific
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline Applications are possible throughout the year. For more information, please see: https
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=27222 PhD Student – Computational Multi-Omics for Precision Oncology (m/f/d) Stellenanzeige merken
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Degree Dr rer nat Course location München In cooperation with Find out about the participating research groups here: https://www.lsm.bio.lmu.de/faculty/current-members/index.html Teaching language