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scenarios and time series analysis for groundwater modelling. Fluent command of English for scientific writing and international collaboration. Ability to work effectively in an international research team
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Description In this project, we develop machine learning models for prediction of optical properties of chiral molecules based on DFT/CCSD data which we calculate ourselves. We include derivative information by
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contributions in one or more of the following key areas: computational modeling of chemical systems, AI-driven materials discovery/design, robotics for chemical synthesis, machine learning applications in
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of concrete samples by alternating short-term model predictions and accelerated aging experiments on reconstructed aged-equivalent samples. The methods to develop and adopt will be: for O1, literature review
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the contracted resource and adoptive parent training program and the contracted home study model. The person in this position is responsible for the delivery of the contracted training curriculum and home study
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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such as formal (mathematical) modelling and/or (quantitative) empirical testing (conducting experimental research and/or large-scale field studies). As an international group of researchers seeking
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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. Are you interested in tackling these challenges with cutting-edge AI sensing techniques? At the AISensing team we focus on AI-powered sensing technology, spanning large-scale AI models, simulation of next
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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