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or others). Aim for apply for funding sources (DGF, BMBF, Bavarian and TUM funding) to strengthen your carrier. 2-years full-time, with option to extend (max 4y). Requirements A PhD or equivalent and a solid
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experience (possibly as part of the PhD) in a field related to mathematical analysis of partial differential equations (PDEs), calculus of variations, optimal control, who is willing to engage in innovative
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. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning
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06.09.2021, Wissenschaftliches Personal The Institute for Communications Engineering is currently offering seven positions for PhD or postdoctoral candidates for the project "6G-life" which is
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scientific trends and results, as input to the strategic planning of the Agency. The team entered the field of computer science and applied mathematics in 2004, performing research related to discrete
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
Experience Requirements The post-doctoral fellow will have received a PhD, DrPH, MD, or equivalent doctoral degree with experience in environmental health, environmental chemistry, environmental engineering
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, communications, and updates. Required Education and Experience PhD Degree in computer science, applied/computational mathematics, engineering, bioinformatics, data science, or a related field and two years
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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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) and present your work at top conferences and journals in our field. Candidates should have completed their Master/Diploma studies in Computer Science, Mathematics, Mechatronics, Electrical Engineering
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, enrichment analyses - biological interpretation of data Your qualification - PhD/MSc degree in bioinformatics, computer science, mathematics, life sciences - background in Machine Learning and/or RNAseq