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genetic screens in Primary and cell line models of T-cell lymphomas and characterize their oncogenic dependencies, and functionally validate them using CRISPR-based genetic perturbation approaches. Through
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: Chetelat, B., Gaillardet, J., Chen, J.Bin, 2021. Dynamic of boron in forest ecosystems traced by its isotopes: a modeling approach. Chem. Geol. 560, 119994. https://doi.org/10.1016/j.chemgeo.2020.119994
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for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models to real-world data (RWD). Create innovative tools and solutions to extract deeper
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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and
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: 87645BR Supervises staff involved in providing clinical patient care. Assists in ensuring the quality of patient care delivery in a specific patient care area and serves as a role model for professional
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of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation of existing approaches for scientific applications
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Project summary: This PhD develops and evaluates a GPT-based AI co-facilitator for Group Model Building (GMB) workshops, supporting stakeholders in constructing Causal Loop Diagrams of complex socio
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Intelligence (AI) models for monitoring and analyzing complex processes, with particular attention to compliance with transparency, interpretability, and reliability requirements. In this context, a systematic
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remain largely unknown. The aim of our project is to use animal models to identify the brain signature of CR, based on the heterogeneity of CR levels observed in clinical trials. To this end, our research
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guidance. Additional responsibilities include mentoring students and contributing to a multidisciplinary research team. The full job description is available, here: https://acrobat.adobe.com/id