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optimization for heterogeneous distributed data, and hardware/software codesigned engines for demanding applications. Main duties and responsibilities Work closely with all members of the lab carrying out
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, and model adaptation strategies. Scalable AI training: distributed training, efficient fine-tuning, evaluation, and deployment on large-scale compute. Multimodal learning: representation learning and
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quantitative and probabilistic extensions, and scalable verification via symbolic automata and related decision procedures Routing algebras: composable, algebraic representations of distributed
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Basel has generous resources for genomic analysis (scientific high- performance computing, data management, training and support). Furthermore, our Department of Environmental Sciences offers a
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networks, including 6G, WiFi, and satellite systems, support robust perception and inference for distributed AI agents, and how the realities of networked operation shape the design of adaptive
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scientific computing frameworks Strong analytical thinking, problem-solving ability, and communication skills Demonstrated ability to work independently while contributing effectively to collaborative
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Employer EPFL SV BMI UPRAMDYA Location Lausanne, Canton of Vaud (CH) Switzerland Salary Competitive Salary Closing date 30 Aug 2026 View more categories View less categories Discipline Computing
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research; Experience with quantitative data analysis and proficiency in one or more programming or scientific computing environments, such as Python, R, or MATLAB; A strong publication record appropriate to
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intersection of applied physics, high-performance computing, and semiconductor metrology. The successful candidate will have the opportunity to develop their own independent research direction and work
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member of the SNSF Starting Grant project NEXUS-3D, working at the intersection of applied physics, high-performance computing, and semiconductor metrology. The successful candidate will have the