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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 2 months ago
. We are assembling a team with diverse expertise and particularly encourage individuals with skill sets in at least two of the following areas to apply: demographic modeling, large language models
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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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: Project A – Synthetic Data for Theory-Driven Behavioral Research This project investigates how large language models (LLMs) produce synthetic responses to psychological experiments and how these compare
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Researcher or experienced Data Scientist to harness AI, machine learning, and statistical modeling on cutting-edge datasets in precision feeding, animal behavior and welfare, multi-omics and environmental
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challenges in medical and surgical robotics that correspond to unmet clinical needs. We pursue two complementary approaches: (i) advancing foundational robotics capabilities in actuation, sensing, surgical
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required) Skill Experience with nutritional, immunological (LPS) or metabolic models in animals. Familiarity with laboratory techniques in biochemistry, metabolomics, or bioinformatics. Exposure to mass
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of the following projects: Project A – Synthetic Data for Theory-Driven Behavioral Research This project investigates how large language models (LLMs) produce synthetic responses to psychological experiments and how
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setups with machining tools and employing additive manufacturing techniques (3D printing) Support and develop experiments, including feasibility checks and technical design with CAD tools and FEA (Finite
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by two ETH professors from two different departments. If you don’t have hosts yet, Design++ can help match you based on your proposal. Project background You are a great fit if you’re excited to: (a
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address fundamental and technological challenges in medical and surgical robotics that correspond to unmet clinical needs. We pursue two complementary approaches: (i) advancing foundational robotics