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Field
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, including ability to prioritise workload and work to deadlines. Desirable criteria Scripting skills and experience in one or more open-source languages (e.g. Python, R) Prior experience/knowledge of cannabis
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in programming (Fortran, Python), and evidence of working well as part of a multidisciplinary research team. You must possess the organisational skills and initiative to conduct independent research
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technology that will make it possible to publish, combine, and preserve statistical models for physics experiments and beyond, and to make them usable across programming languages (C++, Python, and Julia
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, impacts, or resilient system design. Proficiency in programming (e.g., Matlab/Simulink, Python, ROS) and embedded sensing. Good analytical skills, ability to design experiments and prototypes, familiarity
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field. Hands-on experience with free-space optical alignment, THz beam delivery, electro-optic sampling, polarization optics and imaging, or time-resolved pump-probe experiments. Proficiency in Python
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: single-cell fluorescence microscopy, microfluidics, image analysis, and machine learning (as applied to biological imaging). Python and MATLAB programming, especially in image analysis, are also essential
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analysing medical images and working with data Strong programming skills in R or Python Good communication skills and ability to work in a team Ideally, you would also have: Experience with AI and machine
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biology. Experience with single-cell RNA sequencing data analysis, basic scripting in R or Python, and integration of multi-omic datasets is considered an advantage. Candidates should also possess a strong
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, or probabilistic reasoning. Proficiency in Python, C++, or MATLAB, with demonstrated ability to implement complex algorithms. Excellent written and oral communication skills. PREFERRED QUALIFICATIONS Experience with
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, applied mathematics, or image processing, combined with advanced programming proficiency in Python. The ideal candidate will have previous experience in machine learning, particularly in self-supervised