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strong academic record with a solid background in Machine Learning (Deep Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills
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Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A
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optimizations and approaches inspired by machine learning within the framework of cognitive radar; and C) verify the developed approaches with suitable simulations and experimental demonstrations. Specifically
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skills and knowledge needed to complete the project. A critical attitude towards your own work and that of others. Good written and oral communication skills, with the ability to communicate the value
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with the topic of mechanical properties, fatigue of metallic, composite and polymer structures and materials. A desire to acquire the new skills and knowledge needed to complete the project. A critical
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of knowledge institutions, companies, social organisations and governments. Click here to go to the website of the Faculty of Technology, Policy and Management. Conditions of employment Doctoral candidates will
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systems. Working at the faculty means working together. With partners in other faculties, knowledge institutes, governments and industry, both aerospace and non-aerospace. Working in field labs and
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/or radar cross section. Such criteria will be investigated for static as well as dynamic scenarios, where the latter have the advantage that we can use relatively accurate prior knowledge from past
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online. We will not process applications sent by email and/or post. A pre-employment screening can be part of the selection procedure. For the final candidates, a knowledge security check will be part of
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degree in Chemical Engineering Prior expertise in process sytems engineering (e.g. process design and simulation) Prior knowledge of chemical process modeling Ability to work in a team and mentor other