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to the research program. Job Requirements: A PhD in Computer Science or relevant fields. Strong background in deep learning and neural rendering. Excellent programming skills to implement and deploy deep learning
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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | about 13 hours ago
Research Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD project will focus on the characterization of P
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University of Munich, and EURAC Research. Research visits at the partner institutions are foreseen. We are seeking highly motivated candidates with a solid background in databases and a deep interest in
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innovative thinking are indispensable. Qualifications 1. Education and Research Background: PhD degree in Physics, Optical Engineering, Electrical Engineering, or related fields with a background in
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Professional Doctoral Degree, Doctor of Engineering (EngD) in 2021. The programme aims to train students to become IT leaders with deep technical expertise for innovating, designing and managing complex IT
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Social Science or related field Understanding of research methods, statistical software, and database management Supervisory skills Preferred: PhD in Social Science or related field Strong skillset in
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well as providing service to the community and profession. In addition to contributing to the BS in Mathematics, BS in Statistics, MS in Applied Statistics and Data Science, MS in Mathematics, and PhD in Mathematics
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response of ice-marginal lakes in Greenland under climate change. Candidates should be educated to PhD level (or be very near completion) in a relevant subject e.g. Earth Sciences, Hydrology, Data Science
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an accredited institution or foreign equivalent.): A PhD or MFA in media, communication and journalism or similar with an emphasis in film/media arts. Experience : Demonstrated commitment to working effectively
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using newly identified biomarkers, including nuclear F-actin, and rare cell phenotypes identified by deep learning. This prospective longitudinal cohort study has potential for more accurate lesion