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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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Professional Experience: PhD and at least 3 years postdoc experience in relevant areas of expertise. Personal Competences: Diligent, enthusiastic, ability in theory and experimental physics. Summary
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-including evolutionary algorithms, ant colony optimisation, and simulated annealing-to fine-tune an LLM/agent that generates high-quality prompts, inputs, and tool-use strategies for density functional theory
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: Own the optimiser: design, implement, and tune heuristic/metaheuristic algorithms (e.g
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to cover costs associated with taking up employment (flights, visa, insurance, etc.), to be distributed monthly as a top-up to the gross salary.1 · 9,000 EUR relocation for secondment stipend to cover
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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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this short-term project we shall use active learning to accelerate the training of deep learning algorithms for optimising 2D material van der Waals (vdW) structure discovery. The goal is to make model
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. Minoru Otani). The post holder will undertake the following tasks: Contribution to the design, implementation, testing and demonstration of new methodologies and algorithms for the study of electrochemical