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Plan / Goals to be achieved: The candidate will join a dynamic translational research team, within the framework of a strategic collaboration with a pharmaceutical company, with the goal of developing
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of research. These include: Computational physics, including statistical mechanics, biophysics, fluid mechanics, quantum physics, and molecular dynamics Numerical methods for partial differential equations and
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). • Required Skills: 1. Strong background in statistical mechanics and thermodynamics 2. Proficiency in first-principles calculations (VASP, Quantum Espresso) and molecular dynamics simulations (LAMMPS, OpenMM
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research-intensive Universities? You will be part of a dynamic and collaborative research environment within the School of Biomedical Sciences at the University of Leeds, and a member of the vibrant Neur
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of the application. The candidate is invited to upload any additional documents to the “other documents” tab as needed. Skills and Knowledge: Experience with molecular simulations and high-performance computing
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). This pivotal role centres on elucidating the molecular and cellular mechanisms that drive ALS pathogenesis by leveraging cutting-edge methodologies, including the use of patient-derived induced pluripotent stem
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in medicine, and early successes in the field have demonstrated the dynamic interactions among individual microbial species and highlighted the crosstalk between microbiota and their hosts
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. The successful candidate will use very high-resolution simulations (Cloud Model 1) to study the interaction between tornadoes and the built environment to understand how tornado dynamics evolve over urban
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highly motivated and talented Research Fellow to join our dynamic cancer biology laboratory. The successful candidate will work on projects aimed at understanding the molecular mechanisms driving cancer
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sustainability. We develop functional fiber materials with specific photonic and electronic properties by polymer molecular engineering and fiber structure design. Together with advanced textile technology