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the Generative Flow Network (GFlowNet) algorithm. We plan to further enhance this algorithm to consider the shape of biological binding sites and incorporate modules for optimizing physicochemical properties
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strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore
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Interatomic Potentials) code for ternary compounds with variable composition with crystal structure optimization algorithms (evolutionary, random, etc.); - Application of the CSP DFT/MLIP methodology to various
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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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programming languages like Fortran, Perl, HTML/CSS, and Python is helpful. Experience working with large data sets and complex statistical algorithms is strongly preferred. The applicant should be able to solve
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is to discover governing equations from experimental data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help
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. Numerical Analysis and Scientific Computing, developing numerical methods and algorithms that are accurate, efficient, and robust with respect to challenges that manifest in the simulation of complex
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of AI for the integration of multimodal healthcare data specifically incorporating patient preferences. This includes investigating new methods but also designing and benchmarking integration algorithms