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, or workshops. Knowledge, Skills and Ability: Ability to program in multiple programming languages like C/C++, FORTRAN, Python, R or similar scientific programming languages. Knowledge of parallel programming
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optimize large-scale distributed training frameworks (e.g., data parallelism, tensor parallelism, pipeline parallelism). Develop high-performance inference engines, improving latency, throughput, and memory
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to well-known open-source projects or a personal portfolio of impactful open-source research code. Experience with large-scale distributed training and high-performance computing (HPC) environments.
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numerous graduate schools and the MD/PhD program of the CU School of Medicine. Qualifications: Minimum Qualifications: Applicants must meet minimum qualifications at time of hire. Bachelor’s degree in
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Description This PhD project bridges computational neuroscience and machine learning to study the mechanisms of active forgetting—or unlearning—through the lens of both biological and artificial systems
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addition to a vibrant and highly competitive residency program with 25 positions, we offer 9 fellowships and participate in numerous graduate schools and the MD/PhD program of the CU School of Medicine. Our
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algorithms and complexity theory, including in both well-established settings (e.g., sequential computation on a single machine and distributed/parallel computation on multiple machines) as well as emerging
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Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison
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Funded PhD studentships The successful candidate for each studentship will receive full funding for tuition and stipend, at UKRI standard levels. International candidates are encouraged to apply
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further studies as a PhD candidate. Additional programme cost: £500 Fugitive methane in Scotland: Plume mapping and isotopic characterisation using Cavity Ring-down Spectrometry (Dr Adrian Bass) Supervisor