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essential for learning complex distributions over structured data such as text, graphs, and biological sequences. Developing and understanding models for dis- crete spaces is therefore a key challenge in
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cancer cohorts. • Proven ability to develop novel computational and AI-driven methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data
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is available at https://colalab.ai/ . At EI, the group will develop an ambitious Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems
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is available at https://colalab.ai/ . At EI, the group will develop an ambitious Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems
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this pool on an as-needed basis throughout the year and will be contacted if they are being considered for an open position. Individuals in these positions will design, develop, and deliver non-academic
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Banner Financial Aid module, including annual cycle preparation, rules tables setup, packaging algorithms, need analysis parameters, fund management, and overall process automation. Collaborate with
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, and development of algorithms for real-world clinical data. The fellow will have the opportunity to work with clinicians, engineers, data scientists, trainees, and research staff in a highly
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one day a week on campus. Job description The computational biologist will be expected to: Lead the development of acoustic detection and classification algorithms for marine mammals. Evaluate algorithm
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campus. We are particularly interested in researchers working in mathematical artificial intelligence, broadly construed. This includes, but is not limited to: machine learning algorithms, formal proof
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multidisciplinary fields, participation in academic conferences, experience from scientific publishing and other personal and professional development. The project will be carried out at Luleå University