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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be: highly motivated with a record of high scientific productivity, possess
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neuroimaging and fluid biomarkers, (b) systems biology analysis of pathways from multi-omics data using multi-layered network approaches, © machine learning for identification of genetic risk factors in ADRD, (d
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clinically validate innovative predictive models utilizing AI and machine learning; test cadaveric anatomy study implementation Complete Data collection and finalize evaluation of AI predictive models
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The Distinguished Research Fellowship Program seeks PhD graduates from underrepresented groups for postdoctoral experience and training in the School of Engineering and Applied Sciences . The aim of the program is to
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of military Service members with extremity amputations. Why should I apply? Under the guidance of mentor(s), you will gain hands-on experience to complement your education and support your academic and
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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cerebral organoid models to study the impact of environmental exposures on metabolostasis and proteostasis Build and validate machine learning models (in collaboration) for molecular data analysis and
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong