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of quantum computing and an understanding of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s
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Solid experience with statistical modeling, machine learning, or AI Practical skills in R and/or Python for data analysis and model development Familiarity with microbial ecology, genomics, or food safety
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scientific writing and communication skills very good knowledge of statistics and quantitative data analysis, hands-on experience with R or Python strong interest in prototyping commitment to and interest in
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Matlab or Python. Experience with neuroscience techniques (e.g. EEG) is a plus. You possess proven problem-solving skills and an ability to take a strong, positive and proactive approach to work. You have
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at the master’s level, strong programming skills in Python, and experience with at least one of the popular deep learning libraries (PyTorch, TensorFlow, Keras, etc.). Good verbal and written communication skills
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R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches Familiarity with large-scale survey data, conflict and
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data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will
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spectrometry data analysis; experience with omics analyses and coding (e.g. python, R); experience with bacterial genetic engineering or mammalian cell culture is a plus; good project and time management skills
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of X-ray methods Knowledge of X-ray optics Knowledge of synchrotron science Knowledge of catalysis and energy storage Experience with programming languages (ideally Python), SPS controls system Fluent in
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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic