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responsibilities. Experience Essential: E1. Experience of planning and progressing work activities within professional guidelines or organisational policy, applying initiative and independent judgement. E2. Track
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bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1: Fiber-based soft
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, machine learning or causal inference for estimating, understanding and forecasting demographic and health outcomes, at the individual and aggregate levels, including as they relate to life course and socio
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. We take a bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1
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. We take a bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1
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PhD programme are the following: 1. (25%) Merit and excellence criteria: academic track record will be one of the considered criteria (examination and dissertation marks, courses followed, internships
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ingredients); AI & automation in food manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology
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. Interactive, adaptive learning technologies that integrate multimodal input (text, speech, gesture, eye-tracking) to provide personalized, responsive feedback. Human-AI co-adaptation: designing systems where
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(typically mathematics, physics). For Postdoc applicants: Excellent track record in computer science or engineering. Fluency in spoken and written English is required. Proficient in at least one programming
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-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for microbial bioprocess development; microbial