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with electronic structure methods (DFT, TD-DFT, BSE). Experience with scientific software integration and user-facing tools. Knowledge of HPC or parallel computing. Experience with machine learning in
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and resolves performance and scalability issues in large-scale workflows (e.g., optimizing parallelization, data chunking, and storage efficiency for high-volume datasets).
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studies. These pipelines must be capable of efficiently exploiting different types of parallelism, both at the level of a computing node (CPU and GPU) and at the level of a cluster of PCs. This environment
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candidate is at Hebelstrasse 20, 4031 Basel. Projects: Targeting on corpus luteum–derived endocrine signals in decidualization and endothelial function. In parallel we investigate the pathomechanisms
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distinct individuals with varied interests, needs, and abilities. Salary based on course contract. Campus varies. Minimum Qualifications University Parallel: Master’s degree in the discipline or subfield in
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of motif-recognizing proteins. In parallel, the candidate will develop computational approaches to identify indirect interactions based on prior biological knowledge and to infer higher-order complex
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. In addition to the NIHR funded post available, locally funded posts may also be awarded in parallel, subject to funding and assessment against the stated criteria for the role. Applicants should be
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, including parallelization, traceability, reporting and workflow orchestration through Nextflow. Collaborate with data/AI engineers, computational biologists, and experimental partners in both academia and
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, distance, flow, or cut problems that are as well-suited as possible to dynamic, parallel, or distributed computing models. Requirements: Master's degree (or equivalent) in computer science or a related field
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technologies; knowledge of HPC parallel and highly performant clustered or distributed file systems architectures and their effective use and deployment for storage and management of research data lifecycles