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The successful candidate should possess the following: • A PhD in Materials Science, Chemical Engineering, Chemistry, Physics, or related fields • Highly motivated, committed, and capable
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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, and develop high-throughput/automated experimental workflows. - Experience in developing autonomous laboratory systems is a strong plus. Qualifications • PhD in Materials Science, Physics, Chemistry
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. About You Completion or near completion of a PhD in the fields of chemistry, chemical engineering, materials engineering, physics or similar field; Demonstrated expertise in fabrication and
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-throughput first-principles (DFT/MD) simulations, and generative AI to predict, interpret, and design materials for energy storage, energy conversion, and electronic applications. The successful candidate will
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. Mentorship: Assist in supervising 1–2 PhD students or junior researchers and support their research development. Job Requirements: A Master’s or PhD degree in Chemistry, Chemical Engineering, Materials Science
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• A PhD degree in Materials Science, Chemistry, Chemical Engineering, Physics, or a related field. Candidate who is in the final stages of PhD studies (i.e. submitted thesis; pending oral defence
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• A PhD degree in Materials Science, Chemistry, Chemical Engineering, Physics, or a related field. Candidate who is in the final stages of PhD studies (i.e. submitted thesis; pending oral defence
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astronomical nitriles. • Performing data analysis including vibrational spectroscopy, mass spectrometry, X-ray and neutron diffraction crystallography. Perform supporting computations such as DFT calculations
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. Operate within an area of specialism (can include alloy design & modelling – CALPHAD/DFT/ICME, experimental alloy production, advanced characterisation (SEM, EBSD, TEM, STEM, APT, diffraction), and/or