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of high-temperature superconductors and their topological properties. The position is for a full-time employment for an initial appointment for 2 years, extendable to 3 years based on performance. Start date is as per
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performance metrics such as absorption and diffraction across a large design and material space. Curate and format large datasets of simulated nanostructures and their optical properties in close collaboration
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. Evaluate optical performance metrics such as absorption and diffraction across a large design and material space. Curate and format large datasets of simulated nanostructures and their optical properties in
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drive. In particular, we are looking for academic excellence and/or demonstrated scientific achievements. We also expect you to have a strong ability to conceive new ideas, construct high-performance
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(e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal
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of statistical programs (e.g. in Matlab, R or SPSS) German and English skills Proven ability to collaborate effectively, maintain performance under tight deadlines, and demonstrate a high level of commitment and
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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and Performance of Research Experiments (75% of Time Spent) Mechanism based discovery of cancer therapeutics. Characterization of metabolic processes of leukemia stem cells. Use of animal model systems
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include DNA sequence data curation, processing, and analysis using high performance computing; excellent verbal and written communication skills; and a collaborative mentality towards research and mentoring
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this highly collaborative role, your work will be responsible for developing multi-scale models, spanning from the atomistic to the meso-scale, and performing the simulations that will feed into the model