-
. Experience with High-Performance Computing (HPC) clusters, geospatial foundation models, large-scale geospatial processing or cloud-based Earth Observation platforms would be advantageous. You will be a highly
-
processes. Previous experience with data assimilation and numerical modeling will be regarded as positive in the selection process. Experience in automated processing of large data volumes. Experience with
-
data science approaches. Must document scientific programming skills, specifically for data processing and analysis of large datasets. Evidence of creativity and capability of independent research
-
visualization of large geospatial datasets. Translating research data and results into applications supporting sustainable water management and environmental monitoring. Publishing in peer-reviewed scientific
-
/cold environments is an advantage. Previous experience in automated processing of large data volumes such as Earth observation data is beneficial. It is an advantage if potential previous experience with
-
/cold environments is an advantage. Previous experience in automated processing of large data volumes such as Earth observation data is beneficial. It is an advantage if potential previous experience with
-
for big data). Output orientation: Demonstrated capacity to publish in top-tier scientific journals. Ideal (but not required) is experience with practitioner outlets and other high outreach formats
-
with remote sensing data (satellite, aerial, hyperspectral, SAR, LiDAR) Computer Vision Natural Language Processing Remote Sensing Machine Learening and Deep Learning Reinforcement Learning Large
-
, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the