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skills in R and/or Python and experience in statistical modelling of high-dimensional biological data. Experience working in HPC environments and managing large-scale sequencing datasets is essential
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responses (e.g., differentiation, vascularization, and cell–cell communication). Will design genetic circuits and assays; collect and analyze data using techniques such as flow cytometry, RT-qPCR, next
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; mechanism design and institutional design; cooperative AI and multi-agent systems; the study and development of large language models; cross-cultural psychology and large-scale behavioral data; philosophy
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that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks
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comparative research across Mojo, Julia, Rust, and vendor toolchains. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or related field. Experience with LLMs or agentic AI frameworks
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stimulation, and application of pharmacologic compounds during patch clamp experiments. Maintaining the electrophysiology rig, keeping accurate records of experiments, performing data analysis, and creating
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. AI4GI develops tailor-made neural network architectures, including sparse and biologically informed models, to predict disease risk and complex quantitative traits from large-scale genomic data such as
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-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be
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research in focused ultrasound therapy, aiming to achieve breakthroughs in acoustic physics, ultrasound biology, multimodal imaging, intelligent treatment, and clinical big data. Our mission is to develop
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cost, as large amounts of photosynthesis products are allocated to mycorrhizal fungi. This project will aim to quantify the carbon cost of symbiosis in different hosts and conditions, explore