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analyse and visualise the resulting patterns to uncover fundamental principles of neural circuit organization and function. This work bridges computational neuroscience, systems biology, and data
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large
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of Large Language Models Time-Series Data Prediction and Modeling Intelligent Decision-Making and Optimization Algorithms Strong programming skills (proficient in Python, PyTorch/TensorFlow, etc.). Strong
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specialist skills in large-scale data analysis and contribute to high-profile international publications. About you Our ideal candidate will be passionate about contributing to large-scale research in civic
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-degenerative diseases. Our data comes from large public cohorts as well as local proprietary databases of clinical and preclinical medical images, including Positron Emission Tomography, temporal multi-modal MRI
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large spatio-temporal data sets and mechanistically modelling soil C processes. Together with the project team, which consists of a postdoc, a PhD student, a technician, the project PI, and several
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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and experience in: Handling and analyzing large datasets of genetic and coronary imaging data. Apply relevant statistical techniques to analyze registry-ascertained health outcomes in women Execute
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outreach activities. The postdoctoral fellow will join a research group with access to large, administrative data sets and prior experience with big data and administrative data will be advantageous. NMBU
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data or job advertisements, as well as expertise in web crawling, large language models, and text analysis techniques. An excellent understanding of causal inference methods is essential to develop