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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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al. 2019] and point-force Lagrangian models, with advanced post-processings [Vegad2024]. This work will be carried out with the YALES2 high-performance platform. Where to apply Website https
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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learning workflows, and developing complete models. Example applications include drug design, cryo-electron microscopy, structural prediction and dynamic simulation of biological macromolecules, genomics
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predictive models for drug response. Furthermore, we work on creating new treatment stratification methods to personalize treatment for individual patients. More information about the lab can be found here
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generation and predictive modeling by measuring the conductivity and permittivity of diverse electrolytes. The research will be structured into four key phases: (i) the design, fabrication, and validation
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models