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Field
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blend theoretical approaches with hands-on experimentation, as well as collaborate with PhD candidates. You'll design experiments using our robotic platform, analyze tactile data patterns, implement real
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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. The group is dedicated to advancing prognostics and health management (PHM) methodologies for aerospace operations and maintenance. Job Requirements Applicant Requirements Educational Background: A PhD
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”. Are you excited about the opportunities that data science in nutrition—especially the role of targeted metabolic modelling, machine learning and AI offer in developing effective personalized nutrition
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training programs for PhD candidates and consortium members in interdisciplinary and transdisciplinary research methods; Develop and facilitate workshops and webinars to build collaborative expertise across
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of the Functional Materials group and work in close collaboration with colleagues at HIMS. This is what we ask of you You have a creative, curious and driven nature; You have a recent PhD degree in Chemistry
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learning and AI offer in developing effective personalized nutrition interventions? Are you interested in deepening yourself in dietary patterns uncovering what works, for whom, and under which circumstances
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and host quality on disease risk. Applying molecular techniques to quantify fungal abundance and diversity in large-scale biodiversity experiments. Co-supervising PhD students, particularly supporting
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Challenge: How do we enable continuous robot learning in shared
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recognize yourself in this? A strong background in applied physics, engineering, or a closely related field (MSc + PhD). Proven experience in material design, fabrication, and 3D printed structures (e.g