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Postdoc (physics/chemistry/meteorology) (m/f/d) Atmospheric Sciences/Trace Gases FTIR Remote Sensing
Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago1 Mar 2024 Job Information Organisation/Company Karlsruher Institut für Technologie (KIT) Research Field All Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Germany Application Deadline 12 Apr 2024 - 21:59 (UTC) Type of Contract To be defined Job...
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Remote sensing for agriculture and vegetation systems. The successful candidate will work in an interdisciplinary project team that aims to map plant traits (e.g., chlorophyll, nitrogen) and weeds using
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Your Job: You will join the Simulation and Data Lab `AI and ML for Remote Sensing,` which aims to enhance visibility in interdisciplinary research between applications from remote sensing and large
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Simulation and Data Lab `AI and ML for Remote Sensing,` which aims to enhance visibility in interdisciplinary research between applications from remote sensing and large-scale AI with high-performance and
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institute with around 300 colleagues, where diversity, equal opportunities, and a healthy work-life balance guide all areas of work. Flexible work schedules tailored to your needs as well as remote work
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. The department “Lidar” develops ground-based, airborne and spaceborne lidar systems. It applies them for active remote sensing of key meteorological parameters and atmospheric trace gases enabling research in
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have expertise in data analysis, with programing skills in R or Python. Experience with other programing languages, GIS and/or remote sensing are valuable. Experience in project management, and
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: analysis of satellite data, atmospheric remote sensing, calibration, validation Good communication skills, due to cooperation with project partners Good knowledge of the English language. Willingness
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master's studies in a relevant field (geoecology, ecology, environmental sciences, agricultural sciences, physics, remote sensing, etc.) We offer: an interdisciplinary working environment a collegial and
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(e.g., internet and social media data, bibliometric data, crowd-sourced data, geo-spatial and remotely sensed data) to complement existing sources when addressing longstanding population questions;(ii