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transcriptomics and 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
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Objectives: - Completion of the characterization of nanomateriais on in vivo models of vaccination - Completion of the characterization of nanomateriais on in vitro and in vivo cancer models - Analysis
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
transcriptomics. The main projects are the analysis of gene expression patterns in malignant human tumor samples mostly coming from clinical trials and preclinical model systems, and on the continued development
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and researchers to gain insight into novel methods used to predict toxicity of various chemicals and gain understanding of how these chemicals impact in vitro, cell-based model systems. Why should I
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model predictions with biological knowledge and external data sources. Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological
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17 Mar 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Modelling tools Geosciences » Geology Geosciences » Hydrology Researcher
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platforms, environmental prediction models, and visualization tools as needed. Additional tasks include field experiments to test the instruments and validate models; preparing data reports and presentations
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acousto-structural transmission paths, developing predictive models, and producing research outputs that support practical noise mitigation solutions for the built environment industry. Key Responsibilities
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Shell Model with Continuum and its extensions to construct high-fidelity microscopic optical potentials, designed for use in few-body reaction formalisms, in particular Faddeev-type calculations targeting
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non