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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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researcher position with an anticipated duration of 16 months, to work under the direction of Dr. Melissa Lucash. In our lab, we use a variety of tools (e.g. big data, spatially explicit simulation modeling
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required Working knowledge of all aspects of planning and deploying an AI project, including initial problem specification, data gathering and analysis, model creation, and implementation Strong
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and sets up experiments in hybrid research environment. 2. Researches artificial intelligence/machine learning algorithms, database design, deep learning, big data, and cloud computing. 3. Publishes
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language processing), machine learning, or data science. Preferred Qualifications Expertise in Deep Learning and LLMs(Large Language Models): Knowledge in building applications and fine-tuning models like Llama 3
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on hotspot detection. This modeling work is well supported by large-scale primary datasets, including survey-based, parasitological, serologic, and genomic data. Relevant methodologies include mechanistic
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, though exceptional candidates with expertise in human cognitive neuroscience are encouraged to apply. Position Overview Successful candidates will lead projects involving: • Large-scale neurophysiology
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information Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Desired skills, knowledge and abilities: Experience with large-scale molecular dynamics (MD) simulations
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academic health center and entry into the Big 12 Conference, UC’s momentum has never been stronger. UC’s annual budget stands at $1.85 billion, and its endowment totals $2 billion. UC is a mission-driven
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will be evaluated on: • Prior knowledge and demonstrated expertise in next-generation sequencing data analysis, computational systems biology, and/or data integration; • The ability