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methodologically strong and motivated to work at the intersection of applied machine learning, social sciences, and natural sciences. Essential qualifications: A completed PhD in data science, computer
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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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evaluation. Collaborating with heritage partners (e.g., Swiss National Museum, SIK-ISEA) and preparing evaluation reports and a white paper for applied use. PhD in Digital Humanities, Imaging Science, Computer
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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation
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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
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annotation, and emerging machine-learning and generative methods for spectra or structure proposals. Evaluate and test emerging technologies (hardware and software) in close interaction with collaborators and
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between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain is open to discussion. Project B – Understanding and Countering
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PhD on top level in the field of architectural history and theory You bring founded experience in academic reserch and writing You bring appoved teaching experience from one or more academic
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the lab, and at the same time to play an active role in shaping and creating an inspiring research and working environment. In line with our and Uni Basel values (https://www.unibas.ch/en/Research/Values
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to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative image‑based database to manage and