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interviews with participants in French and/or Italian. Accuracy checking and data cleaning of interview transcripts in the language they are conducted in. Reviewing English translations of interview
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You will develop a new coupled solid/ fluid mechanics-based numerical model to simulate the motion of debris flows You will incorporate novel field data currently being collected at numerous
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innovation project with a Swiss industrial partner. The focus is on sensor-based and model-based methods for real-time condition assessment and predictive maintenance of safety-critical mechanical
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for oncology, including tool-augmented inference, multi-agent or compound model workflows, process supervision, verifier-guided training, and reinforcement learning-based post-training. The goal is to
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integration and release engineering Manage the technical release path of trained Apertus models: checkpoint conversion and preparation of release artefacts (weights, configurations, tokenisers, model cards
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submanifold (SSM) methods, aiming to further develop concepts in nonlinear model reduction Possible research directions include the development of nonlinear modelling and control approaches for fluid
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of deep-learning image-enhancement models for real-time operation. Rather than focusing primarily on model training, the successful candidate will work on translating existing and new architectures
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) you will: Manage the ML platform with a data-centric focus, ensuring infrastructure supports the full research lifecycle—from data tokenization to model cartography and validation—rather than just code
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, increasing the demand for long-term rehabilitation and placing significant clinical and economic burden on healthcare systems. The current neurorehabilitation model relies heavily on resource-intensive
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focuses on the development of revealed and stated preference surveys to model passenger response to the effects on train service of planned railway maintenance disruptions in Switzerland. Project