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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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Reference Number: 11180 Fixed-term of 3 years | Part-time with 65% (25 hours/week) | Salary according to TV-L EG13 | European Institute for Molecular Imaging We are UKM. We have a clear social mission and, with our focus on healthcare, research, and teaching, we bear a unique responsibility. To...
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enhanced MRI with computer simulations of image contrast and mass spectrometric imaging of tissue samples and single cells. This project is part of the Collaborative Research Centre 1450 “Insight
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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innovative technology to real-world conservation challenges, working directly with data that informs habitat management and biodiversity protection. Expected start date and duration of employment This is a
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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information and quantum computing? Do you enjoy studying complex quantum mechanical systems both from a physical and a mathematical perspective? Are you an innovative and productive team player who enjoys being
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, to experimental biophysics and biochemistry, and, to cell and molecular biology involving data science. A position comprises 65-75 % of the fulltime weekly hours and is limited until March 31, 2030. The period of