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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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an autonomous framework for setting up, executing, and optimizing complex electronic structure workflows, ranging from ground-state Density Functional Theory (DFT) to many-body perturbation theory methods such as
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or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation of complex socio-technical or organisational systems causal inference, econometric analysis, or formal
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
to local communities and corporations – we turn complex, large-scale, and often messy datasets into actionable insights that shape policy, drive accountability, and create real-world impact. We operate with
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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engage in academic journal writing with the core team to support their own academic careers. The PDRA will have the further opportunity to participate in the Black Health and the Humanities Network (co
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requirements You are an engineer looking to use your analytical and technical skills and innate curiosity to create an impact for a more sustainable urban environment. You also thrive in a complex trans
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competences: Practical experience in molecular cloning, expression, purification of protein complexes and their biochemical and biophysical characterization. Prior knowledge of crystallography and/or single
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, interdisciplinary environments; experience coordinating complex workflows (e.g., between wet lab, proteomics and dry lab) and maintaining structured project documentation. The lab places strong emphasis on teamwork