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problems in medical applications. Specific requirements: Demonstrated experience in Bayesian inference / networks; Familiarity with statistics and probability theories; Excellent research, academic writing
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salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher education, research
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will perform enhanced sampling molecular dynamics simulations using neural network potentials and deploy statistical mechanics (e.g., rare-event theory), Markov-state models for molecular kinetics
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to the ELLIS network. Your work will benefit from interactions with computational, biological, and clinical groups across the institute, alongside opportunities for international collaboration, research
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. The division is an important part of the eSSENCE e-science collaboration and of the Science for Life Laboratory (SciLifeLab ) network, a national research infrastructure for life sciences. The successful
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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understanding of data quality, reproducibility and robust analytical practice. Experience of SQL, cloud-based or high-performance computing environments, and Bayesian methods would also be valuable. Beyond
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diverse set of time-dependent forecasting models (e.g., neural network, mechanistic, statistical, and data-driven) to serve as experts within the integrative architecture. (iii) Mixture-of-experts
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific