Postdoctoral Researcher/KU Data Science Fellows - 10178BR

Postdoctoral Researcher/KU Data Science Fellows

Electrical Engr & Comp Science
Employee Class: 
U-Unclassified Professional Staff

Position Overview

The KU (School of Engineering) Data Science Fellows (KUDSF) is a dynamic interdisciplinary program. KUDFS’s goal is to foster productive multi-disciplinary collaboration among different departments on KU camps and local industry. To reach the goal, KUDSF aims to bring together a wide range of Big Data stakeholders on KU campus to work on difficult and impactful societal problems with data-driven approaches. The University of Kansas has a long tradition of supporting interdisciplinary work. The KU campus houses a group of internationally recognized research centers such as the Information and Telecommunication Technology Center, Achievement and Assessment Institute, Biodiversity Institute, Institute for Policy & Social Research (IPSR) among others and provides an excellent environment to perform data science research.

KUDFS is composed of KU Data Science Postdoctoral Associates and KU Data Science Faculty Fellows. The KU Data Science Postdoctoral Associates can be housed in any department at the KU school of Engineering. The successful candidates are usually co-mentored by two Data Science Faculty members. Postdoctoral Fellows are appointed to an initial one-year term with the possibility of contract renewal with satisfying progresses. In the following non-exclusive list we highlight a few concentration areas on which Postdoctoral Fellows may work.

Focus Areas:
Data genres/data modalities.
Many different data science applications may process the same/similar types of data. For example, linked data (e.g. graphs or networks) are widely utilized in business analytics, healthcare informatics, social science, journalism, city and community management, smart transportation etc. Recognizing the common data modalities in different applications and developing related analytics capability that may be applicable to a range of applications play a central role in data science.

Machine Learning Model sharing, reuse, and serving. With the fast accumulation of modeling algorithm, meta-analysis of a group of machine learning algorithms for applications is important. The related topics in these areas are: automated model construction and parameter tuning, model comparison, model life-cycle management, collaboration in ML model, and model reproducibility.

Open Knowledge Network. In order to support the effective communication between domain experts and modelers, between machine and human experts, and the precise description of machine learning models, it is necessary to construct efficient knowledge representation and related analytics for different applications. Knowledge network is an emerging field that covers topics such as effective knowledge representation, information trustworthiness, and efficient querying methods and reasoning with digitalized domain knowledge.

Fair and transparent data science. Data science products are starting to become widely utilized in many parts of our society. Examples of such data science products including intelligent job hunting, product recommendation, news recommendation, and smart transportation etc. Public sectors start to adopt data science product and utilize them in offering public services. However the modeling processing behind those products is usually opaque and may inherit/reinforce existing bias toward minority groups in our society. Recognizing, preventing, and mitigating algorithmic bias demand interdisciplinary research efforts involving collaboration between technical, policy, and legal disciplines.

Data Science Applications/Collaboration with Industry. We encourage scientists to work on novel data science applications. Such applications may be from areas such as smart and connected communities, smart and connected health, K-12 education among others. Industry collaboration and technology transfer are highly encouraged.

Job Description

65% Conduct research and/or develop systems to conduct research in specific scientific areas consistent with the general research areas of Computer Science, Statistics, Information Science, or other closely related areas.

10% Participate in the activities of the School of Engineering including, but not limited to, group meetings and relevant seminars. Foster collaborations with faculty, and serve as mentor to undergraduate and graduate students.

25% Prepare and/or assist in the preparation of scientific manuscripts for publications and project reports, write protocols, and submit future grant proposals.

Fellow Expectation:

Postdoctoral Fellows should work in a collaborative environment to develop a productive research program involving faculty mentors, research scientists, graduate and undergraduate students, and potentially industry partners.

NOTE: To be appointed at the Postdoctoral Researcher title, it is necessary to have the PhD in hand. Appointments made without a diploma or certified transcript indicating an earned doctorate are conditional hires and are appointed on an acting basis not to exceed 6 months.

Required Qualifications

1. The successful candidates should have an earned Ph.D. (or ABD) in Computer Science, Statistics, Information Science, or other closely related areas.
2. Knowledge of machine learning and data mining is required.

Preferred Qualifications

Working/research experience on large-scale data analytics, system development for data analytics, application development for data analytics is a plus but is not required.

Additional Candidate Instructions

A complete application for the KUDSF program includes the online application along with a cover letter specifically outlining how qualifications are met, a CV, and a research statement.

The review of applications begins October 25, 2917. Applications will be accepted and reviewed until the positions are filled.

Contact Information to Applicants

Jun (Luke) Huan

Advertised Salary Range

Minimum $50,000.00 (Final salary will be commensurate with experience)

Application Review Begins


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Posting Information
Posting ID:
Electrical Engr & Comp Science
Employee Class:
U-Unclassified Professional Staff
Application Review Begins:
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