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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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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 – Beyond Amphiphilicity in the second funding phase
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. Applicants should have experience with tissue culture and standard molecular biology methods. Basic knowledge of computer programming (using the R software environment) and hands-on experience working with
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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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academic and industrial cooperation partners Requirements: excellent university degree (M.Sc., Diploma) in either computer engineering, computer science, electrical engineering or any related natural science
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM/MM simulations [2] Apply the developed code to simulate proton transport in vesicular glutamate transporters
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ties with the local experimental groups of TUD and the institutes of the DRESDEN-concept environment. The chair hosts its own computer cluster and has full access to the high-performance computing
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning