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To Apply: To apply for the position, please provide the following materials as a single PDF file via email to Dr. Andreas Fischer (andreas.fischer(at)uni-greifswald.de): A letter of motivation detailing your
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of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single PDF file to
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
. The group’s work in this area has led to best paper awards at PacificVis and Graph Drawing, with recent publications in IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum
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available; otherwise provide names, affiliations, and email addresses of at least two referees. Applications are reviewed on a rolling basis until the position is filled. Apply early. Incomplete applications
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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regarding the scientific content of the project, please contact Prof. Dr. Chhana Ullah (email: chhana.ullah at uni-goettingen.de ). The University of Göttingen is an equal opportunities employer and places
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the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to martin.horwath at tu-dresden.de or to: TU Dresden, Chair
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like to more widely explore the possibility of computer simulations to refine the targeted synthesis even more and predict the self-assembly even better. Who we are · The Research Training Group RTG2670