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Field
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: Development and application of novel graph learning based methods Development and application of novel AI methods for predicting the synthesisability of organic molecules If appointed at a higher spine point
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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& advance digital twins: Integrate electronic structure (e.g., DFT, ab initio MD, tight-binding) with multiscale simulations to predict experimental observables at interfaces. Bridge theory & experiment: Co
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as (1) Problem Solving and Critical Thinking (Inductive and Deductive Reasoning); (2) Set Theory; (3) Logic; (4) Algebra; (5) Graphs; (6) Theory of Integration and Differentiation; (7) Functions
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differential equations, functional analysis, graph theory or similar areas depending on the evolving needs of the department. The successful candidates are also expected to supervise undergraduate and graduate
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are passionate about any or all of the following: Data Science, Computational Social Science, Behavioral Economics, Human-Bot interaction, Experimental Research, Game Theory, and Artificial Intelligence. Some of
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to students pursuing degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory
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numeric computation. Dynamic programming. Graph algorithms including DFS and BFS. Probabilistic algorithms. Introduction to complexity theory. Graded ABCDE/Y Responsibilities* Enter student assignment and
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of electronic Hamiltonians. The postdoctoral researcher will develop graph neural networks based on the MACE architecture to predict Hamiltonian elements for 2D materials and van der Waals heterostructures, with
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statistical physics. Specific Requirements Preference will be given to candidates with experience in research related to machine learning, graph theory, statistical physics, and modeling of stochastic systems