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.
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 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.
The University of Kansas is a major educational and research institution with about 30,000 students on five campuses, 2,600 faculty members, and service centers throughout the state. EECS is the largest department in the KU School of Engineering, with 36 faculty and annual research expenditures of over $10 million. The main campus of KU is in Lawrence, a vibrant, thriving community of more than 90,000 residents that is just 40 miles west of Greater Kansas City and 20 miles from the state capital. The University of Kansas is an EO/AA employer.
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 Huan
Department of Electrical Engineering and Computer Science University of Kansas Lawrence, KS, 66047-7621 USA
Phone: 1 (785) 864-2375
Email: jhuan at ittc.ku.edu
Web: http://ittc.ku.edu/~jhuan
No comments:
Post a Comment