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/f/d, E13 TV-L, 50-75%) The position is limited for three years. Description of the project The research group of Prof. Dr. Frank Schreiber at the University of Tübingen deals with the physics
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%) The position is limited for three years. Description of the project The research group of Prof. Dr. Frank Schreiber at the University of Tübingen deals with the physics of molecular and biological materials
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, and cutting-edge environment, we are looking for a PhD candidate on the topic of “Automatic recognition of building attributes”. This PhD position is funded through a cooperation with the Munich Re
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-time / part-time full-time Mode of study Less than 50% online Programme duration 6 semesters Beginning Winter and summer semester Application deadline Announced on the website for the summer semester: 15
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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available in the further tabs (e.g. “Application requirements”). Programme Description Higher education graduates of outstanding academic merit who plan to complete a doctoral programme or carry out
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. Please send them under the subject ”PhD Behaviour” by e-mail to Prof. Dr. Karen Alim (k.alim@tum.de) by 30.05.2022. She will also be happy to provide you with further information in advance. The position
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Max Planck Institute for Plant Breeding Research • | Koln, Nordrhein Westfalen | Germany | about 4 hours ago
Full-time / part-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline There is one application call per year. Details on the timeline can be
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structured PhD within an interdisciplinary and internationally connected research environment. The Global Partnership Network (GPN) offers a graduate programme in which our PhD students are co-supervised by