Research Associate Position Openings(Clinical Informatics)

We are seeking a highly motivated Research Associate(Clinical Informatics) to join the biostatistics team at Case Western Reserve University.

POSITION OBJECTIVE

The Clinical Application and STatistical LEarning (CASTLE) Lab focuses on statistical method development and collaborative work in biomedical and human health research. The lab will closely work with interdisciplinary investigators to explore clinically impactful questions in the fields of cancer, cardiovascular disease, kidney disease, and neurodegenerative disease (e.g., Alzheimer’s’ disease, Parkinson’s disease) by utilizing state-of-art statistical methods and developing tools for clinical practice.

The lab is seeking a research associate to assist the PI in all aspects of the execution of lab work and promote research in biostatistics and quantitative healthcare.

The Research Associate STS is expected to perform data management and analysis, coordinate the lab activities, oversee collaboration, carry out assigned research projects through completion under the guidance of PI, and work independently with research engagement. The research associate has the opportunities for (PhD and MS-level) student mentoring, (co-authored) paper publications and is also encouraged to conduct methodological research of his/her interest if time allows.

Specifically, the research associate will keep the PI informed of progress of research and collaborative projects, present data and findings at lab and scientific meetings, seminars, etc., help on manuscript preparation, and contribute preliminary data for grant proposals.

The research associate should have basic education and training background of statistics, biostatistics, (applied) mathematics, data science, computing science or other quantitative analytical fields. Preference will be given to a highly motivated and skilled candidate with specific expertise in longitudinal data analysis, survival analysis, causal inference, biomarker identification, data integration, risk prediction, big data analysis (e.g., Electronic Health Record, National Cancer Registries) and software package development.

The CWRU SOM is affiliated with some of the best hospitals in the United States, such as University Hospitals Case Medical Center (UHCMC), Cleveland Clinic Foundation (CCF), The Metro Health System (MHS) and Louis Stokes Cleveland Department of Veterans Affairs Medical Center. Through these partnerships, cutting-edge technologies, research facilities and collaborative opportunities are accessible to our research team. The recruited candidate will have chances to work with researchers in the Case Comprehensive Cancer Center (CCCC), Cleveland Alzheimer’s Disease Research Center (CADRC) and Cleveland Clinics Foundation (CCF).

ESSENTIAL FUNCTIONS

  1. Assist PI in developing novel statistical methods, computational algorithms and webtools for application. In particularly, work with PI on extensive simulation studies to evaluate the empirical performance of the proposed methods, implement the methods with efficient algorithms, and disseminate the package with online tools for broad application. (30%)
  2. Work PI on collaborative research with the ability to move forward the projects independently. Responsible to oversee the progression of collaborations, supervise students on analysis, and ensure assigned projects are being completed according to the research plan. Attend project meetings with collaborators and prepare reports under the guidance of supervisor. (30%)
  3. Help PI on manuscript preparation and write-up, contribute preliminary data for grant proposals, prepare the annual reports of research grants and contract proposals and/or post-award reports to funding agencies. (15%)
  4. Present data and findings at lab meetings, journal clubs, scientific meetings, seminars, etc. Ability to identify high-quality data resources with data exploration, assembling and processing for scientific papers and grant proposals. (10%)
  5. Motivation for independently author research projects. With the highest level of integrity and responsible conduct of research, participate in writing manuscripts as a first author and/or co-author for discoveries from research performed in the lab, together with the PI and other members. (10%)

NONESSENTIAL FUNCTIONS

  1. Perform other duties as assigned (e.g., paper review, team website maintenance) (5%)

CONTACTS

Department: Frequent contact with PI and lab members, work with other faculties for collaboration of research projects, contact with administrative staff as needed

University: Regular contact with other investigators across the university for collaboration; Occasional contact with the Animal Resource Center; Contact with other departments as needed

External: Supply vendors, other institutions, funding agencies, etc. as needed

Students: Undergraduate and graduate student employees working in the lab

SUPERVISORY RESPONSIBILITY

No direct supervisory responsibilities. Will train and provide oversight to lab members.

REQUIRED KNOWLEDGE, SKILLS and ABILITIES

  1. Excellent analytical skills with experience using statistical models in longitudinal and survival analysis as well as machine learning techniques for real data applications; ability to formulate findings and recommendations from the analysis.

  2. Excellent oral and written communication skills and interpersonal skills; must demonstrate the ability to effectively and professionally communicate and work with various individuals within and external to the University.

  3. Strong organization skills; ability to multi-task, prioritize, and meet deadlines. Must demonstrate attention to detail and accuracy, time management skills, and follow-through. Must be able to work under pressure and conform to shifting priorities, demands, and timeline.

  4. Ability to work effectively independently and collaboratively within a team. Must be highly motivated

    Apply - Interfolio

Liangliang(Lyon) Zhang
Liangliang(Lyon) Zhang
Assistant Professor

Dr. Zhang’s research interests center around Bayesian inference and prediction, high dimensional models, and complex structured data, such as brain imaging and metagenomic data.