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first experiments of the future quantum-computer technology that is orders of magnitude more efficient than existing quantum processors. Join us in shaping the future! As a result of five ERC grants
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modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process
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sustainability, performance, and reliability. Our research leverages optimization techniques, applied machine learning, and statistical analysis to achieve these objectives. Through the DecAI project we will work
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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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, are expected to rapidly acquire the necessary domain knowledge and be interested in building a career in the field. The postholder will work closely with the experimental team in the School of Biological
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machine learning tools and working on Linux High-Performance Computing platforms would be highly desirable. This is a highly collaborative role and you will work with scientists and clinicians from other
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signatories of the Armed Forces Covenant and welcome applications from service people. Further information Before submitting an application, you may wish to discuss the post further by contacting Elaine Bignell
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. The role will also provide the opportunity to learn and develop skills in structure-based drug design. The role is ideal for someone who has, or is about to be awarded, a PhD in structural biology and would
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46 Faculty of Philosophy and Education Working hours: 20 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: permanent Reference no.: 4367 Explore and teach