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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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Observation data analysis. You are fluent in Python and have solid experience with relevant machine learning and geospatial libraries such as PyTorch, TensorFlow, GDAL, rasterio, and zarr. You have experience
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evaluation.This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, and
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the Division of Data Science and Artificial Intelligence and the employment is with Chalmers University of Technology. The division’s research spans from foundational machine learning theory to applications
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Python, including use of data analysis libraries such as pandas and scikit-learn Experience with Large Language Models (LLMs): using APIs (e.g., OpenAI, HuggingFace), or local models (e.g., Ollama
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale biomedical and biological data, including developing and optimizing models to predict disease progression and create
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finance, numerical analysis, machine learning and data science, or the Oxford Centre for Industrial and Applied Mathematics (OCIAM); you must clearly state at the start of your supporting letter which
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(including computer science, machine learning or deep learning). Activities Description of the research activities : The post-doctoral researcher will develop the research actions defined in his/her research
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learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large medical datasets (e.g., electronic health records data or medical images) Ability to use high