Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
undergraduate or graduate researchers. · Experience writing research grant applications. · Experience with software such as R, Python, Matlab, and GIS tools · Experience facilitating workshops
-
University of the Virgin Islands | Saint Croix Falls, Wisconsin | United States | about 11 hours ago
data collection using the SigCap Application. Data processing and analysis in Python. Assess how environmental conditions and context (weather, indoor/outdoor settings) influence reception quality
-
data collection using the SigCap Application. Data processing and analysis in Python. Assess how environmental conditions and context (weather, indoor/outdoor settings) influence reception quality
-
biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models
-
the Collaborative Doctoral Partnerships programme, training researchers at the science-policy interface. Where to apply Website https://jobs.unibas.ch/offene-stellen/phd-position-ai-driven-pathways-to-health
-
SWAT+gwflow Proven experience in modelling natural and human-induced drought (streamflow and groundwater drought). Proficiency in programming (R, Python, or MATLAB) and GIS tools. Experience in climate
-
Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales. The Inverse Modelling group at the Department
-
., examples of program codes (Python, R, SQL, GIS scripts, etc.), description or documentation of technical solutions to research problems in which the applicant participated (spatial data processing
-
for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
-
for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity