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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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R and/or Python, with experience in data integration and statistical analysis. Exposure to RNA therapeutics or functional genomics approaches is an advantage. Strong interest in interdisciplinary
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skills, particularly in Python and/or R · Experience in machine learning and AI · Fluency in spoken and written English · Ability to work independently as well as in interdisciplinary teams Meritorious
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programming language, preferably Python or R. Experience in any of the following areas: large scale sequence analysis, microbial genomics, human gut microbiota research (shotgun metagenomics), Metagenome
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for seeking the position. Extensive experience with Python, Matlab, and R, and good UNIX knowledge are essential skills, as well as familiarity with biological omics data analysis techniques. Experience with
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, genetics, plant biology, or a related field, and have documented familiarity with computational analysis (Python or R and HPC environments), and a demonstrated, strong interest in genomics and/or polyploidy
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Python and/or R for bioinformatics and ability to write clean, reproducible, and well-documented code for complex multi-step pipelines. have documented experience from cancer research. Additional
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analysis, and biology, as well as Python and R programming language (critical). Previous experience analyzing high-resolution spatial VDJ/transcriptomics, long-read sequencing, single-cell transcriptomics
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, mathematics, applied mathematics, computer science, biomedicine, biotechnology, or another relevant field. Documented experience in programming or quantitative data analysis, for example in Python, MATLAB