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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and resource-efficiency requirements. This collaborative doctoral project brings together
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description 1–2 PhD students in
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demonstrate suitable experience in computer science, machine learning, robotic vision, or a related field (through a high-quality Honours or Masters degree). The successful candidate must be able to enrol as a
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performing local neural pattern recognition and decoding. A low-power transceiver, which will transmit only the decoded high-level control commands instead of the raw data, dramatically reducing bandwidth and
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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local gas/liquid phase conditions. Whilst direct simulations of breakup are possible, computational cost is high, restricting applications to small sections of geometry and for modest run times
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students on the campus, offering the chance to engage and connect - our PhD Representatives offer information and advice when needed access to high-performance computing a chance to contribute meaningfully
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-value reinforcements in their short and randomly aligned form. A key challenge to the effective reintegration of recycled carbon and glass fibres into high-performance products lies in achieving scalable