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. Requirements: Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas Expertise in computer vision and/or machine learning (deep learning
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available in the further tabs (e.g. “Application requirements”). Programme Description Applicants holding a PhD or who are eligible to be a junior professor in the historical humanities are invited to apply
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The INM – Leibniz Institute for New Materials in Saarbrücken, Germany, is an internationally leading center for materials research, a scientific partner to national and international research institutions, and a research and development provider for numerous companies throughout the world. The...
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
’) are compared to ‘post-impact’ investigations, covering a period from days to years after simulated manganese nodule mining. The focus of the position advertised here in the third phase of the project will be
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14.12.2022, Wissenschaftliches Personal The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics
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information, please contact Prof. Dr. Ulrike Cress via phone: +49 7071 / 979-209 or by email: u.cress@iwm-tuebingen.de . Please send your detailed written application, including a cover letter, curriculum vitae
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available in the further tabs (e.g. “Application requirements”). Programme Description The programme is intended for outstanding junior post-doctoral scientists from Germany, Austria and Switzerland from all
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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, this position can be filled part- or full-time. In the case of equal qualification, applicants with severe disabilities will be treated preferentially. Please send your application documents (cover letter
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genomic approaches Application of the modeling approaches in relevant downstream tasks Co-development of high-performance computing AI training codes for the first European Exascale Supercomputer JUPITER