249 molecular-modeling-or-molecular-dynamic-simulation positions at Monash University
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an integrated scalable framework that supports model evaluation, assess the performance and hyper parameter optimisation based on clinical and molecular data cohorts. The project will also intend to adopt
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This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal
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this work we will use Theory of Mind (ToM) to reason about the mental models of the actors involved in AI systems, designers, agents and users and expand on the "Model Reconciliation Problem" or reconciling
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the people who discover them The Opportunity We are seeking a passionate Research Assistant to join our cutting-edge research team in molecular science within Monash Biomedicine Discovery Institute (BDI
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The proliferation of misinformation and disinformation on online platforms has become a critical societal issue. The rapid spread of false information poses significant threats to public discourse
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to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in ‘evolving’ data. Over the last two decades, many algorithms have been proposed
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financial barriers to achieving theoretically optimal city sizes using qualitative methods, including stakeholder interviews and policy analysis. Integrating Theory into Dynamic Models Embed the sustainable
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. Experience in cell culture, molecular biology, microscopy, and flow cytometry is desirable, along with strong organizational and communication skills. If you are passionate about contributing to high-impact
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study the underlying theory, using the framework of Evolutionary Game Theory and build models for concrete applications based on this theory [2]. The ultimate goal of this project is to develop new
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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them