Post-Doctoral Associate
Job Description
Position Number:
129057Title:
Post-Doctoral AssociateFunctional Title:
Post-Doctoral AssociateCategory Status:
15-Fac.Non-Tenured,Continuing ConApplicant Search Category:
FacultyUniversity Authorized FTE:
100Unit:
AGNR-Plant Science & Landscape ArchitectureCampus/College Information:
Founded in 1856, University of Maryland, College Park is the state’s flagship institution. Our 1,250-acre College Park campus is just minutes away from Washington, D.C., and the nexus of the nation’s legislative, executive, and judicial centers of power. This unique proximity to business and technology leaders, federal departments and agencies, and a myriad of research entities, embassies, think tanks, cultural centers, and non-profit organizations is simply unparalleled. Synergistic opportunities for our faculty and students abound and are virtually limitless in the nation’s capital and surrounding areas. The University is committed to attracting and retaining outstanding and diverse faculty and staff that will enhance our stature of preeminence in our three missions of teaching, scholarship, and full engagement in our community, the state of Maryland, and in the world.
Background Checks
Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify you from employment.
Position Summary/Purpose of Position:
The Postdoctoral Associate will work at the University of Maryland (College Park) in the Shahoveisi’s lab and will work on applied and basic research projects related to turfgrass disease identification and management. The postdoctoral associate will be expected to be an independent and collaborative team member in research and extension activities. Candidates should have background and expertise in one or both of the following fields:
Plant and Pathogen Genetics:
Skills including conducting PCR and qPCR tests, bioinformatic tools for variant calling, genome-wide association studies (GWAS) and bi-parental mapping analysis.
Machine Learning and Statistical Analysis:
Proficient in conducting and troubleshooting machine learning analysis using large image or numerical datasets for disease identification and prediction via software such as Python and R. Also, background in field design and statistical data analysis in the context of agriculture and applied plant pathology.
Key responsibilities:
- Designing and conducting research objectives in the Turfgrass Pathology Program.
- Collect data, conduct analysis, interpretation of results under the PI’s supervision.
- Publish and present research findings in scientific journals and conferences.
- Collaborate with interdisciplinary teams within and outside the institution.
- Contribute to the development of research proposals and grant applications.
Benefits Summary
Top Benefits and Perks:Minimum Qualifications:
Education:
- PhD in Plant Pathology, Genetics, Computational Biology, Compute Science, or a related field at the commencement of the position.
Experience:
- Demonstrated expertise in one of the two specified research areas.
- Knowledge of programming languages such as R, Python, or similar.
- Publication record in relevant scientific journals.
- Proficiency in statistical analysis and bioinformatics tools.
- Excellent communication and teamwork skills.
Preferences:
Preferences:
- Familiarity with plant disease epidemiology and risk map development.
- Experience in turfgrass science.
- Ability to work independently and manage multiple aspects of the project.
Posting Date:
02/19/2024Open Until Filled
YesBest Consideration Date
03/13/2024Diversity Statement:
The University of Maryland, College Park, an equal opportunity/affirmative action employer, complies with all applicable federal and state laws and regulations regarding nondiscrimination and affirmative action; all qualified applicants will receive consideration for employment. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, national origin, physical or mental disability, protected veteran status, age, gender identity or expression, sexual orientation, creed, marital status, political affiliation, personal appearance, or on the basis of rights secured by the First Amendment, in all aspects of employment, educational programs and activities, and admissions.
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