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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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share your interests? Maybe you can become our new colleague at the Department of Process, Energy and Environmental Technology at The Faculty of Technology, Natural Sciences and Maritime Sciences
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to two PhD students in Statistics for the research project “Next-Generation Latent Variable Models for Social Data Science”. Application deadline: November 4, 2026. We are seeking one to two PhD students
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of business administration and economics. We focus on real-life issues that have a huge impact on society, economics and ecology: from robotics to big data, and from job market participation to change
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
, the University of Copenhagen and the MAX IV synchrotron. You will work with an international team of mathematicians, crystallographers and data scientists. The project aims to transform small-molecule structure
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In this PhD position, you will help design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed
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the BioGeomorphology Laboratory flume, field datasets from restored and natural saltmarshes across the UK, and expertise from researchers in coastal ecology, hydrodynamic modelling, AI, and data science. Objectives
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learning, computing, data science, biomedical engineering, or a related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with
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spectrometry workflow, from sample preparation and digestion through LC-MS/MS acquisition to data analysis and interpretation; a laboratory animal science certification (Article 9, rat/mouse) that is valid in
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(incl. roads, railways, and waterways) as well as their associated engineering structures (bridges, retaining walls, noise barriers, and hydraulic structures). A wide range of remote sensing data may be