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, collaboratively developing your own research line and publishing in leading machine learning conferences (e.g., NeurIPS, ICLR, ICML) and scientific journals; Actively build bridges with experimental groups within
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learning for interconnected systems (e.g., 6G and Edge AI platforms, self-driving vehicle vision) in collaboration with industry partners and domain experts. This PhD thesis is offered in the context
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Vacancies PhD on Integrating Sustainability and Systems Thinking in Engineering Education Key takeaways The CLEAR initiative (Chemistry Learning for Environmental Action and Responsibility) is an
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) at the Faculty of Health, Medicine and Life Sciences. TGX is an interdisciplinary research environment where computational scientists, molecular biologists, clinicians, and nutrition researchers collaborate
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learning experiences. Part of your work will involve designing and testing new educational interventions in collaboration with teaching staff. You will work in a multidisciplinary team environment, and
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to obtain this; You are proactive in developing research collaborations and have the ambition to acquire external funding in interdisciplinary settings; You have excellent communication skills and are fluent
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and collaborative project work. Your main activities will include the following: Learning to evaluate mission and system design architectures by supporting reviews and understanding how complex
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collaboration with industry partners and SMEs. You have a track record of scientific publications in relevant venues. You are proficient in Python and relevant machine learning frameworks such as PyTorch
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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of training activities and actively contribute ideas to improve our programmes. Your responsibilities will include: supporting the implementation of training and learning activities in close collaboration with