49 estimation-methods "https:" "Computer Vision Center" Postdoctoral research jobs in Switzerland
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- ETH Zürich
- University of Basel
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- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
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- EPFL - Ecole Polytechnique Fédérale de Lausanne
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optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs. Agentic and tool-augmented AI systems: develop
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analysis methods for realistic CO2 electrocatalysis, with a focus on parallel investigations and accelerated aging. Dissect degradation processes of CO2 electroreduction catalyst, electrodes, and ionomer
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project aimed at advancing our single-cell ribosome profiling technologies in cancer. For further information about the lab, please visit https://www.sendoellab.org/. The Institute for Regenerative Medicine
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(ALD) reactors. The range of simulations focuses on finite element methods and can include Monte Carlo based simulation approaches and continuum modelling. Verification of simulation results against
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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of electronic devices has a long and successful history of accompanying experimental developments, be it for transistors or memory cells. Nowadays, to be of practical relevance, such technology computer aided
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an interest in how psychological theory can improve synthetic data and in deepening our understanding of when and why LLM-generated responses approximate human behavior. The project involves a collaboration
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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) develops advanced optical technologies, including imaging, spectroscopy, and laser ablation methods. Our goal is to bridge these cutting-edge laser technologies with clinical practice, developing solutions
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it independently You must be willing to exchange with your fellow team members, support them, and learn from them Non-traditional research topics, questions and methods are encouraged We also encourage