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Position Description The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to computational analysis of mass
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computational biology or biological data analysis. Strong candidates may come from protein-focused, cell-state-focused, or multimodal biological modeling backgrounds and will have expertise in one or more of the
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measurements (e.g., release kinetics, seed vigor) – 20% Data analysis, interpretation, and manuscript/report preparation – 15% Coordination of research activities and mentoring of undergraduate assistants – 10
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are focused on Systems Biology, Epigenetic Gene Regulation, Single-Cell Genomics and Cancer Biology. Responsibilities for this position include data analysis, method development, scholarly activities and other
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qubit engineering to quantum algorithmic analysis to applications across the physical sciences (condensed-matter or high energy physics, data science). You will Interpret, report, and present research
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-static and Quasi-static simulations) Discrete electronic design (Analog and digital design using COTS, schematic and layout design, microcontroller programming, PCB prototyping) Experimental data analysis
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to the society. Renewal is based on performance and the availability of funding. Duties Develop and design novel computational and AI methods for single-cell genomics data analysis, protein design, etc
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printed circuit board prototyping Basic data analysis using MATLAB or Python Experience with microcontrollers Problem solving skills and out of the box thinking A combination of education and relevant
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are focused on Systems Biology, Epigenetic Gene Regulation, Single-Cell Genomics and Cancer Biology. Responsibilities for this position include data analysis, method development, scholarly activities and other
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analysis and characterization Notice of Non-Discrimination and Equal Opportunity The University of Delaware does not discriminate against any person on the basis of race, color, national origin, sex, gender