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for publication ● Pursuing independent research projects in conjunction with the focus of the labs Qualifications ● PhD in a field falling within the social, behavioral, cognitive, and/or environmental sciences
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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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for the Modeling of Biological and Sociotechnical Systems (MOBS Lab) within the Network Science Institute at Northeastern University. MOBS Lab focuses on research projects aimed at developing innovative mathematical
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determinants of health. The focus of this role is on cognitive decline and dementia, with an emphasis on: Applying epidemiologic, econometric, and other methods to strengthen causal inference Working with
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collaboration with graduate students and external partners. MINIMUM QUALIFICATIONS Knowledge of complex systems, network science, bioinformatics, and computational modeling, with the ability to design and
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behavior, human-computer interaction, psychology, computer science or related disciplines to work on research projects on human-AI interaction. Increasing ability to generate human-AI interaction data needs
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://tanglab.sites.northeastern.edu/ Qualifications: Having a PhD degree from all science and engineering majors, especially Mechanical Engineering, Chemical Engineering, Physics, and Materials Science. Highly motivated. Having
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About the Opportunity SUMMARY The lab of Professor Albert-László Barabási is looking for Postdoctoral Research Associates in the area of network science, nutrition, biological networks, machine
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written communication skills. -Attention to detail and training in research methodology and quantitative data analysis. -Recent or upcoming Ph.D., or equivalent degree, in Cell Biology, Molecular Biology
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using a variety of cognitive and cognitive neuroscience approaches (e.g. behavioral, psychophysical, neuropsychological, physiological, imaging, pharmacological, genetic, and computational methods