Distributed Computing jobs

Design, develop and deploy services that estimate the environmental impacts of large-scale distributed computing. The ODISSEE project and Avalon team extend energy-only measurements toward life-cycle and multiple-impact assessments, validating tools on the SLICES-FR infrastructure across the IoT, network, edge and cloud continuum. Required skills include…

Develop communication-efficient, privacy-preserving distributed learning algorithms for streaming data in edge inference networks. The PRIDeL collaboration connects Inria, Simula and NYCU, combining theory or experiments on convergence, generalization, data freshness and differential privacy. The doctorate must be defended by 31 December 2026, and the…

Work with the PHIQUS team on distributed quantum computation, quantum correlations and nonlocality, or cryptographic protocols. Mathematical approaches include noncommutative polynomial optimisation and operator algebras. Projects are tailored to the researcher’s expertise and connect with European quantum-information collaborations. The page describes…

Develop optical-fibre and nanophotonic devices for quantum optics and quantum electronics. Research also includes distributed quantum computing and networks combining cold neutral atoms with nanophotonic cavity systems.

Develop GPU benchmarking and supporting computing infrastructure for high-energy physics. The work helps compare heterogeneous hardware used by the Worldwide LHC Computing Grid.

Build and investigate networks connecting superconducting quantum processors. The doctoral work uses superconducting circuits and cryogenic systems to explore quantum communication and distributed quantum operations.

Develop optical links connecting atomic and solid-state qubits for distributed quantum computing. Experimental work combines laser-cooled neutral atoms, rare-earth-ion crystals and trapped-ion systems to study remote entanglement and quantum gate teleportation. The full-time appointment lasts two years with possible renewal. Work may be on-site, hybrid or…

The RIKEN Center for Computational Science seeks technical scientists to develop programming environments for supercomputers, including accelerator-based systems and Fugaku NEXT. Work covers compilers, performance tools, mathematical and communication libraries, AI-assisted software development and application performance evaluation. Researchers collaborate…

Develop scalable machine-learning methods and infrastructure for the Brain Health initiative. Integrate digital histology, spatial transcriptomics, MRI and clinical data to model brain-disease progression at cellular resolution. Responsibilities include foundation models, distributed training, robust data pipelines and open-source scientific tools. This…

Translate frontier machine-learning prototypes into scalable systems for drug discovery. The role optimises models such as diffusion networks, transformers and DNA-sequence models in JAX or PyTorch, improves distributed training and inference, and develops research libraries and evaluation tools.

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…

Research scalable storage and memory architectures at ORNL’s National Center for Computational Sciences. Projects include telemetry-driven optimisation, DDR and high-bandwidth memory trade-offs, I/O performance analysis and composable storage for scientific AI and edge-to-HPC workflows. The researcher collaborates with internal and external teams and…

We seek a Scientist I to build the computational backbone of a lab-in-the-loop platform that designs and validates synthetic enhancers for precision cell-type targeting in the brain. In this cycle, model-designed sequences are tested experimentally, and the results retrain the next round of models. You will contribute to an open-source codebase for…

Build and scale foundation models for single-cell biology and drug discovery using large perturbation datasets, including Tahoe-100M. Work focuses on distributed training, model performance and dependable research infrastructure that helps connect gene-regulatory patterns to cellular responses. The role is listed as on-site and requires at least three days…

Implement and optimise machine-learning systems for AI-driven drug discovery. Collaborate with research scientists on model development, distributed training, inference and evaluation, including architectures such as transformers, graph neural networks and diffusion models. The role combines experimental research with reliable engineering tools and full…

Four-month, full-time research fellowship developing machine-learning infrastructure and reinforcement-learning methods. Projects can address training performance, distributed systems, synthetic-data and environment pipelines, or model generalization. Fellows receive researcher mentorship, a weekly stipend and benefits. Applications are reviewed on a…

Build and maintain HumanBase, a platform used by biomedical researchers to investigate molecular mechanisms of human disease. This hands-on backend role translates computational biology methods into production software, scalable genomics pipelines and machine-learning workflows. Responsibilities include data processing, infrastructure, engineering practices…