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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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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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approaches are gaining importance for autonomous vehicles. However, the training and certification of autonomous systems with machine learning components is a huge challenge, since the learned behavior is
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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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(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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researcher will be responsible for performing statistical analysis of complex datasets derived from large-scale biological images, as well as proteomic and genomic data. The postdoctoral researcher will
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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
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economy. Key Responsibilities Acquire, process, manage, and integrate field and satellite data (Sentinel-2, Landsat/HLS). Develop, calibrate, and validate predictive models of vegetation fuel moisture
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» AlgorithmsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in one of the following areas (or related fields): * Machine learning / deep learning * Quantum computing / quantum