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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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technological developments in your professional field and, together with colleagues and partners, translate these into solutions that meet market needs. Your team You will be part of the Food, Gut & Health Models
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on the integration of BIM, artificial intelligence and predictive maintenance (PM) for intelligent BIM models, digital construction sites, predictive analysis and immersive interactions, outlining an operating
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, predictive analysis and immersive interactions, among others. Where to apply Website https://www.poliba.it/it Requirements Additional Information Eligibility criteria Eligible destination country/ies
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for the next 2.5 years at the interlink of prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
learning models for antimicrobial activity prediction (e.g., Weka); - Strong communication skills; - Fluency in English (written and spoken). The candidate must demonstrate interest in microbiology and
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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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assignment of a research grant, with one position(s), under the project CENTRO2030-FEDER-02359500, title ENDOSWEET - Sugars and polyols generated endogenously as predictive markers of Type 2 diabetes