12 machine-learning Postdoctoral positions at Northeastern University in United-States
-
-scale epidemiological datasets using statistical and machine learning methods Conduct systematic literature reviews and meta-analyses on disease dynamics topics Develop and validate predictive models
-
chemical discoveries for renewable energy, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing
-
growth for Portland, Maine, and northern New England. We are nurturing an environment for high-impact research and innovation in computer and data science, digital engineering, the advanced life sciences
-
About the Opportunity This job seeks a postdoc to work on one or more projects related to the development and assessment of wearable robotics. Possible projects topics include: Developing machine
-
. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
-
internally funded grant development and application activities connected to machine-learning-driven approaches to low-cost, low-carbon structural design. Additional Information The College of Arts, Media and
-
. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
-
expected to develop and lead projects. Ideal candidates will have knowledge of population genomics, machine learning, and evolutionary theory. Candidates should have a strong track record of publication; be
-
), electrophysiology, genotyping, brain stimulation (tES, TMS), computational modeling and/or machine learning. For all our projects, we seek post-doctoral researchers who aim to take leading roles in projects
-
Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific