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to be enrolled in a study programme leading to a PhD (or in a non-degree programme). - Have proven experience in physical and digital modeling to predict the structural performance of packaging, enabling
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, with about half being women. It also includes around two dozen PhD students, Post-Doctoral and Research Fellows, and Associate Professors II. Read more about HUP section at: https://www.sv.uio.no/psi
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Screen reader users may encounter difficulty with this site. For assistance with applying, please contact hr-accessibleapplication@osu.edu . If you have questions while submitting an application
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Screen reader users may encounter difficulty with this site. For assistance with applying, please contact hr-accessibleapplication@osu.edu . If you have questions while submitting an application
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establishing yourself in the field. Candidate Profile: A PhD in Power Electronics is desirable or a minimum Masters level qualification with some industry experience (both in power electronics). Skills
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proposed research directions 1-page statement describing your approach to engaging and growing the CNM scientific user program RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 9 days ago
science. Your profile PhD in Computer Science, AI, Human-Computer Interaction, Computational Biology, Software Engineering, or a related field (or MSc with substantial professional experience). Strong
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supporting a broad range of interdisciplinary user experiments as part of an international team of physicists and engineers Develop advanced concepts for numerical and experimental optimisation of electron
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also expected that the post-doc ill support in the management of on-going research projects, for which previous experience is highly valuable. Co-supervision of PhD students is also foreseen
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models