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! You will demonstrated expertise in developing machine-learning interatomic potentials (MLIPs) for large-scale molecular dynamics (MD) simulations of materials. Together, we will push the boundaries
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, and quantum information , ultracold quantum gases , Ultrafast Molecular Dynamics , Ultrafast Physical Chemistry , Ultrafast Science , Unconventional and topological superconductivity , X-ray
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to work at the forefront of multidisciplinary science, integrating mathematical modelling and data science with diverse disciplines, including ecology, plant physiology, and molecular biology. Your research
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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implementation is advantageous (especially if in the context of protein structure) Experience with structural biology and/or molecular dynamics is advantageous Publicly available code is advantageous Experience
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to forward a copy of your CV along with your academic referees. Informal enquiries to:Colin.clarke@nibrt.ie or niall.barron@nibrt.ie Funding:Annual stipend of €25,000 plus fees for 4 years.Project 2: Model
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of computational chemistry. Applicants can have a background from cheminformatics including RDKit, machine learning applied to chemistry, and molecular modeling Our group and research- and what do we offer? Our
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equipment e.g. STM. Simulating fabrication methods. Collaboration with other groups at NQCP and companies/academic groups in and around the Copenhagen area. Join us in this major confluence of exciting