184 data "https:" "https:" "https:" "https:" "Inserm" positions at Nature Careers in Germany
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(e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications
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Joint Univers. Professorship for “Sustainable polymer matrix composites at TU Hamburg” combined with
degrees must be submitted with proof of equivalence. For information about BAM and subject-related questions, please contact: Prof. Dr. Ulrich Panne, President of BAM (phone: +49 30 8104-1000, email
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about and presenting scientific data Interest in working in a collaborative network of friendly scientists around the world Our offer The position offers an extremely international, well-equipped, and
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experimental studies using animal models and human cells Planning and conducting animal experiments Analyzing, documenting, and presenting experimental data and results Actively participating in scientific
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, CRISPR-Cas systems, microRNAs, non-coding RNA, RNA biology of infections, and RNA chemistry. Applicants can choose a mentor who best matches their interests and background (more information under “Panel
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world leading researchers to Hamburg, starting new and intensifying existing collaborations Please feel invited to forward the information to your international collaborators or submit your nomination
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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contributions to mastering the global challenges of mankind in the fields of energy, mobility, and information. For this, about 10,000 employees of KIT cooperate in a broad range of disciplines in research
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directly by the specialist department in which you could work in the future. More information about our application process Please send your application only via our online application tool and with
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of Antarctic Marine Living Resources (CCAMLR), which aims to manage the fishery sustainably through ecosystem-based approaches that incorporate data on predator populations, ecosystem state, and krill abundance