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Field
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Berkeley Lab’s (LBNL ) Biological Systems and Engineering (BSE ) Division has an opening for a Computational Postdoctoral Fellow to join the Quantitative Modeling Group led byHéctor García Martín
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pipelines, cloud services, dashboards, web-based application development). Experience in handling real-world datasets and model development/deployment workflows. Domain expertise in building construction
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Model Development Support development and evaluation of learning-based control policies using reinforcement learning, imitation learning, and visuomotor learning approaches. Assist in model training
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Python, C++, or Julia, with strong signal processing, numerical modeling, and data analysis skills; experience with Git-based workflows and AI/ML for science is a plus. Hands-on experience in structural
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packages: 1. Development and validation of a clinically relevant oral wear protocol. Establish a reproducible protocol using defined antagonists, saliva-based media, controlled temperature, cyclic force
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within three months of starting in the position. The doctoral programme includes relevant courses corresponding to approximately six months of study, a thesis based on independent research, active
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computing models (non-von Neumann architectures) as a Methode for quantifying computational properties based on emergence and complex dynamics in nanomagnet systems. Nanomagnet metamaterials, Artificial Spin
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Institute for Translational Medicine and Liver Disease | Strasbourg, Alsace | France | about 1 month ago
cholangiocarcinoma and is based on key scientific discoveries described in: Mailly et al. Nature Biotech 2015, Hamdane et al. Gastroenterology 2019, Juehling et al. Gut 2021, Crouchet et al. Nat. Commun. 2021, Roehlen
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challenges. Working within a collaborative, inclusive and high-performing research environment, you will help advance process modelling, soft sensor development, process control and comminution technologies
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control solutions within the mineral processing industry. Experience in developing soft sensors and process models for mineral processing applications. Familiarity with applied process modelling tools