High-performance computing

SC26 connects researchers and practitioners developing high-performance computing, networking, storage and scientific data analysis. The programme includes research presentations, tutorials and workshops at McCormick Place in Chicago, 15–20 November 2026. Early rates run through 14 October. A digital participation option is available through the official registration page.

…connects agents to analysis pipelines, high-performance computing, databases and research protocols, supporting multi-omics and spatial-data work. Responsibilities include assessing correctness, reproducibility and privacy on realistic tasks, deploying useful tools and helping researchers use them. The researcher joins Masoomeh Rahimpour’s team and collaborates with Jonas Demeulemeester’s Integrative Cancer Genomics laborato

…algorithms, scientific software, high-performance computing and scientific machine learning. It takes place in Pittsburgh on 22–26 February 2027. Travel-support applications are open until 30 November 2026. Abstract deadlines have passed; registration is scheduled to open in December 2026. The meeting is co-located with ACDA27 and the International Meshing Roundtable.

…quantum simulation, inverse problems, high-performance computing, dynamical systems, model reduction, optimal transport, network science, tensor methods and related applications. Minitutorial and minisymposium proposals are due 26 October 2026 and contributed presentation abstracts due 23 November 2026.

Develop computational algorithms and software that integrate high-performance computing with experimental imaging, scattering and time-resolved data to study biomolecular dynamics. The Computational Structural Biology Research Team collaborates with experimental groups on biologically relevant molecules. Appointments begin on or after 1 October 2026, with annual renewal possible through March 2030. Applications remain open until filled. Follow the role page’s document-upload, notification and referee instructions.

Research confidential computing and secure multi-tenancy for high-performance computing. The National Center for Computational Sciences seeks system-software architectures for secure enclaves on supercomputers, together with scalable identity and key management. Applications are made on the employer’s recruitment portal; no closing date is stated. Initial appointment up to 24 months, with extensions dependent on performance and funding.

…The role connects high-performance computing, cloud technologies and AI platforms used in scientific work.

Develop numerical algorithms and software for scientific supercomputing. The research includes eigensolver methods for emerging high-performance computing environments.

Funding for collaborative projects that integrate quantum computing into workflows using high-performance computing and artificial intelligence. Projects should address practical bottlenecks and demonstrate benefits of quantum-classical approaches. Projects can last three years from a fixed start of 1 June 2027. The maximum project full economic cost is £3.75 million, with EPSRC funding 80%. Applications close at 16:00 UK time on 3 November 2026.

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybri

The generation of appropriate parameter configurations for high-dimensional hierarchical spiking neural network models is currently one of the slowest and most frustrating tasks in the computational neuroscience workflow. Moreover, many of the currently used methods for multidimensional parametrization only provide single-point estimates, which do not allow deeper insights into the parameter regions of interest and parameter interactions. Having better knowledge about this would allow neuroscientists to understand more about the network dynamics and the relationships between structure and func