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Field
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate
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to advance knowledge in the following domains: Formal verification and interactive theorem proving (Rocq, Lean) Secure and high-performance computer systems, including ML infrastructure We seek outstanding
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for molecular systems and thin films in combination with AI-driven process optimization. Help us shape the future of chemical research! Self-driving lab platform for photochemical and porosity research Join a
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Engineering at the University of Manchester. The project aims to produce ultra-thin polymer-based membranes and fluidic chips with polymers of intrinsic porosity (PIMs), to study molecular and ionic permeation
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• PhD in Chemistry, Materials Science, Chemical Engineering, Physics, or a related discipline. • Strong experimental research skills with a good publication record. • Experience in advanced
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State’s flagship land/sea/space grant research university with an enrollment of approximately 18,000 students. The Physics Department has 22 full-time faculty/staff members and offers BS, MS and PhD degrees
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Primary Supervisor: Dr. Christopher Birkbeck This PhD project, based at our Norwich campus, offers a unique opportunity to work at the confluence of number theory, representation theory, and formal
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Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal