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a passionate community collectively aiming to bring novel technologies and science to cutting-edge numerical weather prediction. ECMWF has been the first operational weather centre globally to
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Monitoring Duties & Responsibilities: Build early warning and clinical deterioration prediction models Develop continuous clinical risk trajectory modeling frameworks Model time-series data such as vitals
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to cutting-edge research projects aimed at collecting massive protein fitness measurements in service of developing a new generation of predictive models linking sequence to function. Key responsibilities will
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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | 26 days ago
, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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models. The candidate will be jointly supervised by Dr. Iris Groen (www.irisgroen.com ) and Prof. Cees Snoek (https://www.ceessnoek.info/ ). Want to know more about our organisation? Read more about
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm for testing enzyme stability
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experimental design. Deep expertise in predictive modeling, classical ML algorithms (e.g., decision trees, gradient boosting), large language models (LLMs), generative AI, MLOps, and AutoML using frameworks like
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Modelling (WCPM) . Find out more about the research group at https://koczorbenda.wordpress.com/ Requirements and eligibility: Applicants must have, or be predicted to obtain, a good degree (2.1 or 1st class