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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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into the setting of metabolic flux inference and, with inspiration from existing algorithms, develop tailored MCMC algorithms. You will implement the ensuing algorithms in an existing C++ framework, validate and
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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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candidates which are explored in more depth. In particular you will work on the extension, development and analysis of new quantum algorithms for near-term and fault tolerant quantum computers for drug
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems