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25 Mar 2026 Job Information Organisation/Company ETH Zürich Research Field Computer science » Other Mathematics » Applied mathematics Technology » Energy technology Researcher Profile Recognised
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More
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; ● Experience compiling, processing, and analyzing large spatial fisheries datasets and/or fishing fleet behavior (via e.g., AIS data) in management contexts; ● Demonstrated understanding of how insights from
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, and microbial resource management. The work comprises the analysis of temporal dynamics of bacterial and archaeal communities, large-scale sampling and quantitative molecular analyses, as
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(Research Assistant) or PhD degree (Research Associate) in computer science or a related area or equivalent experience. Familiarity with standard machine learning libraries/data analysis, specifically as
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outputsDisseminate results through scientific publications in top AI tier-venuesProvide guidance to PhD and MSc studentsContribute to teaching and advanced seminarsDesign and executing large-scale experimental
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connected large wind energy system dynamic modelling, control and analysis. In particular, the objective of this research programme is to lay the foundations of a new, model and methodology for Advanced wind
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. We are currently analyzing brain, CSF and blood, multi-omic data (transcriptomics, proteomics and metabolomics), from a large collection of well-characterized samples to identify novel biomarkers and
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pillars such as Big Data Analytics and Management, Machine Learning and Optimization, AI for Data Science, Deep Learning, Generative Learning, Biometrics Processing, Natural Language Processing, Computer
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interest) in nanopore based DNA methylation analysis and previous bioinformatics training. Requirements: PhD in a STEM subject. Candidates with degrees in neuroscience, data science, bioinformatics, and