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of salinity data collected during the field surveys and through the monitoring stations; Calibration and validation of analytical and numerical hydrodynamic models based on the collected data. The models have
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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project, aims to contribute to the development and preclinical evaluation of new platinum-, palladium-, and nickel-based anticancer metallodrugs, using metabolomics approaches based on Nuclear Magnetic
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commercialization of AI products, with a strong focus on Large Language Models (LLMs) and Agentic AI systems. This role sits at the intersection of advanced AI research, real-world business impact, and product
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and
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-time/Part-time) (several posts) [Appointment period: each for six months] Duties The appointees will assist the project leader in the project - “Developing an LLM-based multi-agent language learning
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 5 hours ago
just medicines. Research Project: Under the guidance of a mentor, you will implement two-stage meta-analytic models and weighted least squares correlation methods to estimate the surrogacy strength
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the intersection of sepsis, microbiology, molecular biology, and renal physiology. Our laboratories use the kidney as a model organ to understand how microbial signals shape tissue function, host responses, and
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-rich carriers of doxorubicin siRNA targeting oncogenes, as well as plasmids expressing tumor suppressor genes, to reduce breast cancer growth in a mouse model. These therapeutic agents will be evaluated
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, CRISPR-based approaches, transfection/transduction, and in vitro bacterial infection models. Apply omics approaches (e.g., epigenomics, transcriptomics, proteomics) to support drug targetidentification and