Sort by
Refine Your Search
-
records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records
-
performance in accordance with the respective service level and application of internal processes. This includes contributing to risk management definition, mitigation actions and lessons learned exercises
-
required. Experience with the design, development and verification of TT&C and PDT subsystems for space applications is required. Very good knowledge of modern computer systems, simulation and modelling
-
testing. Expertise with analogue electronics design, computer-aided design (CAD) or electromagnetic simulation is an asset, as is experience of working on projects and in large teams. Knowledge and
-
very good knowledge of on-board digital signal processing techniques and technologies for RF payloads and microwave instruments will be considered an asset. Very good knowledge of modern computer systems
-
for a four-year assignment. During this time, you will be actively working and learning on the job and will benefit from valuable mobility and developmental opportunities that will prepare you for a
-
technologies, for example, using machine learning techniques to support long term exploration; Topics related to ‘off world living’, e.g. human factors, design and concept illustration; Crew Health and
-
technologies, for example, using machine learning techniques to support long term exploration; Topics related to ‘off world living’, e.g. human factors, design and concept illustration; Crew Health and
-
Advanced control, optimisation and estimation techniques Artificial intelligence and machine learning techniques for AOCS applications and engineering AOCS modelling, AOCS software, AOCS hardware and
-
-up; Perform the tests according to the test plan; Data analysis and reporting. In this project you will learn about space science instruments, space science detector technology, performance characterization