With the fast evolving technology for data collection, data transmission, and data analysis, we observe that the scientific research community is undergoing a profound transformation where discoveries and innovations increasingly rely on massive amounts of data. New prediction techniques, including novel statistical, mathematical, and computational techniques are enabling a paradigm shift in scientific investigation. Deep analysis of large data sets has become a critical endeavor of science by offering complementary insights in addition to theory, experiment, and computer simulation.
We seek highly motivated Postdoctoral Associates to join our efforts to advance machine learning and data mining for better modeling of large amounts of data from scientific domains. Our hypothesis is that much more information is embedded in big data than what can be processed by a few domain experts. We further argue that big data are often linked, and exploring the relationships among data sets enables us to build much better models for understanding structure in data. Details can be found at http://www.ittc.ku.edu/~jhuan/postdoc.html.
We are working on a number of highly interdisciplinary projects to test our hypothesis. Sample projects include:
- Prediction of the role of small molecules in biological systems using chemical structure and biological activity information
- Identification of drugs for new therapeutic strategies
- Delineation of genetic relationships from high-throughput sequencing data
Successful candidates should possess the following qualifications:
(i) a Ph.D. degree in computer science, statistics, bioinformatics, or cheminformatics.
(ii) familiarity with machine learning and data mining theory and algorithms (supervised learning, parametric and nonparametric approaches, MLE and/or Bayesian, model selection and model evaluation),
(iii) strong communication skills,
(iv) excellent programming skills.
Knowledge of biological or pharmaceutical data is a plus but not required. Knowledge of computer systems (high performance computing and cloud computing) is a big plus but not required. The initial appointment is for one year, and the position may be renewed based on satisfactory progress.
To apply, please send your CV, including a list of publications, past and current research experience, and contact information for three references, to Dr. Jun (Luke) Huan.
Dr. Jun (Luke) Huan, This email address is being protected from spambots. You need JavaScript enabled to view it. Department of Electrical Engineering and Computer Science The University of Kansas Lawrence, KS, 66047-7621 USA