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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses from visual and auditory cortices recorded over multiple days Apply and
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and programming languages (e.g. R, python) and HPC Excellent interpersonal and communication skills across clinicians, wet-lab, and computational teams Desirable criteria Experience supporting
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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for ML / LLM algorithms working over free-text clinical data and potentially mixed modalities (waveform, DICOM images etc.) Data manipulation and analysis capability, e.g. machine/deep learning in R/Python
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with imaging-based data. Experience with data science tools, analytics and programming languages (e.g. R, python) and HPC Excellent interpersonal and communication skills across clinicians, wet-lab, and
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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and programming languages (e.g. R, python) and HPC Excellent interpersonal and communication skills across clinicians, wet-lab, and computational teams Desirable criteria Experience supporting
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for ML / LLM algorithms working over free-text clinical data and potentially mixed modalities (waveform, DICOM images etc.) Data manipulation and analysis capability, e.g. machine/deep learning in R/Python
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in Environmental Modelling, Land Use modelling or another relevant field, with clear skills highly complementary to those of the JPP4JL research team Proven ability to write code in R or python