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. Knowledge of Python, C++, Matlab or ROS would be of benefit. The project involves extensive fieldwork, and candidates should enjoy working outdoors. How to apply Please submit a CV and cover letter with your
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, R, or Python is desirable but not essential. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our
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blow moulding. You will also have: Experience applying AI or coding tools (e.g. Python or MATLAB) to analyse scientific data A track record of conducting and interpreting experimental work The ability
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science. They should have a track record of or potential for research excellence. They should be enthusiastic about software development and have working knowledge of python and relevant deep-learning libraries (e.g
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related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You
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knowledge of programming in R and/or Python. You will be self-motivated with proven organisational and time management skills and have the ability to work within multi-disciplinary teams and independently
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proficient in R, Stata, or Python for statistical analysis and have demonstrable experience in handling and analysing large-scale datasets, including EHR and biomarker data (e.g. UK Biobank, China Kadoorie
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or Planetary Science, Applied Mathematics, Physics, or a closely related field, completed by the start of the appointment. You should have strong scientific programming skills (Julia, Python, Fortran or C++) and
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Python, along with working with large datasets. One of the roles will focus on using natural language processing methods applied to large-scale text data; the other will focus on econometric estimation and
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–200) desirable, as well as experience in TEM/STEM image processing and simulation using open-source Python packages such as atomic simulation environment (ASE) and abTEM. A willingness to occasionally