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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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Material Science and Engineering, Mechanical Engineering, Physics or Chemistry Knowledge and experience in direct ink writing technology, thermoelectric and ceramic materials, powder technologies, and
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with prior experience in either fungal or transposon comparative genomics, who can explore the diversity and evolution of Starship transposons and the genes and transposons they carry within plant
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, biochemistry, computational biology, systems biology, or a related field. Strong hands-on expertise in high-resolution LC–MS/MS, with proven experience in metabolomics, lipidomics, or exposomics. Demonstrated
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related field Strong background in experimental AMO physics or quantum optics Experience with cold atoms, optical tweezers, lasers, or quantum control techniques is highly desirable Strong interest in
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department, uniting theoretical and experimental biology with advanced bioengineering and fostering close collaborations with leaders in material science, physics, and chemistry to drive transformative
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Advanced programming skills in Python, with deep experience in libraries such as PyTorch, Pyomo, and the broader scientific stack (Xarray, Pandas). Knowledge of MATLAB is desired but not a requirement A
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description The successful candidate should demonstrate a solid academic background and publication record, as well as experience in high-precision isotope analyses of geological samples. The position will
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politics, using, among others: Econometrics, field and survey experiments, and quasi-experimental causal inference methods, Natural language processing (NLP), and Comparative case studies As part of the ERC
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connectivity Profile PhD in neuroscience, biology, bioengineering, or a related field Strong background in cell culture, including primary neurons or iPSC-derived cells Experience with electrophysiology (e.g