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/or High Performance Liquid Chromatography (HPLC) to monitor cell culture media composition, and how to use these measurements to build predictive models of cell cultures able to infer and optimize cell
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multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh Rahimpour
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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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workers, as required or needed in the completion of daily tasks in support of the assigned area's/department's overall goals and objectives. Uses analytics, predictive modeling, and market research
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238) Where to apply Website https
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for transportation prediction, system optimization, and environmental/health impact modeling Deployment of decision-support tools for public-sector clients (municipalities, MPOs, DOTs) Urban mobility, equity
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new vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands
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are analysed using big data and business intelligence applications to monitor tourisms. Additionally, predictive modeling methods are applied to estimate tourist mobility behavior and movement patterns between
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hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to refine machine
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy