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research. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a background in Natural
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mathematics. Strong programming skills in Python. Ideally, also proficiency in at least one major deep learning framework (e.g., PyTorch, JAX). An early track record of research (e.g., a high-quality Master's
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the appointment. You should have experience applying machine learning methods to biological or genomic data, strong programming skills (preferably in Python and/or R), and an understanding of population genetics
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in epidemiological or population health research, and significant experience analysing large, complex health datasets using statistical or programming software such as R, Stata, SAS, Python, or SQL
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architecture, with knowledge of technologies such as Microsoft Fabric, Purview, Azure and Python, and awareness of LLMs and AI-ready data patterns relevant to Data Architecture. Architecture Delivery: Experience
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Python and/or C/C++/Fortran. Strong motivation to work in an interdisciplinary environment at the interface of theoretical chemistry and quantum technologies. High level of self-motivation, commitment, and
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the role is the development of automated experimental workflows and instrument control using Python. You will contribute to our existing laboratory automation framework by integrating hardware control
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to implement modern software development pipelines using industry-standard tools. We have a particular interest in expanding our capacity to support our students with C/C++ and Python programming alongside teams
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financial or economic data using Stata, R, Python or MatLab. We offer an occupational pension scheme, generous annual leave, hybrid working, and excellent training and development opportunities. For further
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social sciences Excellent knowledge of the following statistical software programs: R and Python Experience in science communication and knowledge transfer (preferably with a focus on teaching scientific