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quality photometry (from optical to near-infrared wavelength) for roughly 100,000 sources. This dataset represents an ideal input to train different machine learning algorithms to classify objects based
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completing a PhD in a relevant field such as data science, AI, Computer Science, machine Learning, Earth System Science, Climate etc.. with the subject of your thesis being relevant to the description
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Education You should have recently completed (within the past five years) or be close to completing a PhD in a relevant field such as data science, AI, Computer Science, Machine Learning, Earth System Science
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-handling systems (such as mass memories and instrument control units) for payloads and platforms, microelectronics (such as FPGAs and ASICs), Artificial Neural Networks, AI and Machine learning, cubesat
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, machine learning or deep learning. Additional requirements You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as
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Trainee, you will develop most of your activity in the field of machine learning and, in particular, neurosymbolic computing. Neurosymbolic AI combines symbolic reasoning with neural networks to enhance
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solutions c) areas of improvement in the context of HCI (Human-Computer Interaction) applied to knowledge management systems d) ESA’s cultural diversity, its working norms and preferences for learning
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maintenance and evolution of the tools described above in order to maintain a complete overview and control of the Unit’s software infrastructure; Generating lessons learned and contributing to the knowledge
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relevant programmes to deliver analysis derived from Digital Twin Models (or replica). Using innovative Earth system models, cutting-edge computing, satellite data and machine learning, DestinE will allow
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learning path that complements their academic education. The ESA Academy programme comprises of three main segments: An academic TRAINING programme which offers individuals a portfolio of highly qualifying