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Field
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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., Python/Julia/C++), including experience with scientific computing tools Ability to communicate research results clearly in written and oral English Desired qualifications: Experience with reinforcement
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policy, economics, statistics, or a related quantitative field Additional Qualifications Strong skills in Stata, R and/or Python and experience analyzing complex data Demonstrated experience working with
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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must
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and CHIP-seq. · Expertise in downstream analysis and biological interpretation of bioinformatic findings · Proficiency in R/Python programming, developing analysis pipelines and
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& simulation Bayesian networks & uncertainty quantification ML frameworks (PyTorch, TensorFlow, Hugging Face, OpenCV) & Python Automated pipelines for multi-modal data Continuous, bi-directional data flow
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in Python and/or R Experience with cloud computing and high-performance computing environments Ideal Candidate Profile We are seeking a computational biologist, biostatistician or bioinformatician with
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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Engineering received within the last 3 years. Essential experience includes Python, modern techniques in visual recognition and image segmentation, and the ability to rapidly prototype and fine-tune open-source
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analysis, ideally using Python-based tools and frameworks, with familiarity in areas such as statistical modelling, machine learning, computer vision, or optimisation approaches for experimental systems