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(negotiable) Group or Departmental Website: https://urbanresilience.stanford.edu/ (link is external) How to Submit Application Materials: Interested candidates should apply at https://forms.gle
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). Require skills Strong background in communication networks and wireless/satellite systems; Knowledge of machine learning and optimization techniques; Experience with simulation tools (Python, MATLAB, ns-3
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(Arduino, Raspberry Pi, STM32), MATLAB/Simulink, industrial sensors and motor drives, HMIs, and CAD software. Application Materials Applicants should submit: Cover letter describing applied teaching
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skills in Python, MATLAB, LABVIEW or comparable technical computing and simulation environments Experience in laser spectroscopy is an advantage Familiar with material characterization such as XRD, SEM
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quantitative data analysis ● Strong technical expertise and independence in programming, with proficiency in tools such as Binary Ninja, IDA Pro, Matlab, software radios, LaTeX, etc. ● Demonstrated
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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on performance. Please take a look at the type of work Prof. Stantcheva does on her website: https://www.stefanie-stantcheva.com/ as well as on her Social Economics Lab website http://socialeconomicslab.org
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support staff from the Sustainable Agricultural Water Systems ARS-USDA unit (https://www.ars.usda.gov/pacific-west-area/davis-ca/sustainable-agricultural-water-systems-research/ ). Research (90%): Field
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floodplain models. Experience with computing technology (e.g. Matlab, FORTRAN) and data management. Demonstrated written and oral communication skills for both technical and non-technical audiences. PREFERRED
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statistical analysis of neuroimaging data. Implement, document, and maintain analysis scripts using Python and/or MATLAB. Integrate cognitive, affective, behavioural, and neuroimaging datasets to test