Big Data Predictive Modeler - OMICS (Heilman)

Position Information

An experienced Predictive Modeler is sought with applied knowledge in developing and implementing high throughput phenotyping techniques and other omics for more efficient and integrated genetic analysis. As a Predictive Modeler individual will strengthen our team by helping develop applications/procedures that enable the use of high-throughput data for genetic/genomic analysis and predictive modelling. The ideal candidate will have strong expertise in quantitative genetics, statistical genetics, data collection (‘classic' and ‘modern') and preparation, genomic breeding value estimation/genomic prediction in plant/animal breeding and large-scale genome-phenotype association studies (GWAS). Experience in breeding program designs and net merit indexes utilizing genomics to make faster genetic gain would be advantageous. This work will contribute to the application of these methodologies in the development of commercial varieties across different crops, as well as its application in livestock production. Individual will also provide expert advice in the implementation of genetic evaluation of field trials and population design. As part of this team, candidate will work with a group of professionals including bioinformaticists, mathematicians/statisticians, software developers, animal scientists, agronomists, microbiologists, and breeders/geneticists that are part of a very creative decision support team. This position is funded by North Dakota State University main station and will support the mission of the newly formed Big Data Pipeline unit.

Task and responsibilities:

• Structure an UAS operational workflow that includes the preparation of the fields and experimental designs, equipment selection, acquisition and operation, and data management and analysis.
• Develop algorithms for the implementation of novel phenotyping data coming from spatial, real-time, and repeated measures.
• Improve our current analytical capabilities utilizing data collected across locations and years, while accounting for spatial field trends, and exploiting genetic correlations between quantitative traits, locations, years, and different experiments.
• Collaborate with the members of the Big Data Pipeline Unit to implement high-throughput analytic tools into internal analysis systems and pipelines.
• Contribute to the development of codes and its integration in analytical pipelines and deploy quantitative-genetic models and methods for the analysis of phenomics and genomic data to improve genetic gain in NDSU breeding programs.
• Independently perform statistical analysis, programming, and predictive modeling.
• Recommend appropriate statistical analyses and methods for genetic evaluation to be applied to different crops.
• Build cross-functional relationships to collaboratively partner on delivering projects and data assets, particularly with agronomist, breeding and animal scientist.
• Collaborate with NDSU plant breeders, plant pathologist, agronomist, and microbiologist to understand biological questions and develop genetic-statistical solutions to complex questions.
• Stay up to date of developments done in academic/industry research on technologies and methods that accelerate breeding programs and exploit these developments to improve internal breeding capabilities
• Accountable to ensure that analysis tools integrate in digital workflows and pipelines.
• Develop R Shiny applications for customized data analyses, visualizations of data and results.
• Work collaboratively with external academic peers and contract research organizations.
• Demonstrated ability to publish original research in peer-reviewed journals.

Advertised Salary:

$70,000/year or as commensurate with experience.

Minimum Qualifications:

• M.Sc. or PhD. in Quantitative Genetics, Animal or Plant Breeding, Biostatistics, or related field.
• Strong background in phenomic-assisted selection.
• Strong background in statistics applied to genetic analysis.
• Experience working with high-throughput phenotyping data collection methods.
• Holistic understanding of high-throughput phenotyping data collection in the context of plant/animal breeding.
• Experience in the analysis of field trial data, GWAS and Genomic Prediction.
• Excellent programming skills, preferably in R and Python.
• Ability to work individually, as well as a part of a multi-cultural team.
• An enthusiastic and customer-oriented team player with excellent verbal and written communication skills.

Preferred Qualifications:

• A solid foundation in quantitative-genetics and applied statistical analyses. Including mixed linear models, Bayesian models, dimension reduction techniques, and machine/deep learning methods.
• Demonstrated knowledge of R, ASReml-R, Shiny, Python, or related programming and analysis tools, and the ability to manipulate large data sets.
• Knowledge of transitioning from ‘classic' to ‘high-throughput' data collection in terms of field requirements and preparation, experimental design, data management and maintenance.
• Good knowledge of established software (Metashape, Pix4D, QGIS, OpenCV, or related) used for UAS data preparation, mosaicking, transformation (e.g., spectral indices), extraction, and analysis.
• Basic knowledge of plant physiology and electromagnetic radiation behavior, as well as their consequences on image-based phenotyping.

Applicant Materials Required:

• Cover letter showing how all minimum qualifications are met and highlighting preferred qualifications that are met.
• Resume/CV.
• Unofficial transcripts of coursework for all university degrees.
• Names, titles, addresses, phone numbers, and e-mail addresses for three professional references.

Application Deadline:

Initial screening of applicants will begin on September 23, 2022 and continue until a suitable candidate is selected. Applications received by the screening date will be ensured full consideration.

NDSU offers a comprehensive benefits package wihich includes single or family health insurance coverage with premiums paid for by the university - with an effective date for new employees the first of the month following date of hire. Other benefits include, wellness benefits, basic life insurance, retirement plan, tuition waiver, annual leave, EAP, sick leave and holiday pay. Optional benefits include supplemental life, dental, vision, long-term care insurance, flexible spending account, and supplemental retirement plans.

About Us:

North Dakota State University is distinctive as a student focused, land-grant, research university. We educate leaders who solve national and global challenges by providing affordable access to an excellent education that combines teaching and research in a rich learning environment.

NDSU is in the highest Carnegie Classification of Institutions of Higher Education, R1, granted to the top research institutions in the country.

NDSU has offices across the state of North Dakota, and the main campus is in Fargo, on the Minnesota border. Along with Moorhead, Minnesota, Fargo forms one of the largest metropolitan centers between Minneapolis and Seattle. The community is consistently ranked as one of the best places to live.

Mission

We provide transformational education, create knowledge through innovative research, and share knowledge through community engagement that meets the needs of North Dakota and the world.

No Smoking Notice:

As an employer, the State of North Dakota prohibits smoking in all places of state employment in accordance with N.D.C.C. § 23-12-10.

EO/AA Statement:

North Dakota State University is an Equal Opportunity employer and all qualified applicants will receive consideration for employment without regard to age, color, gender expression/identity, genetic information, marital status, national origin, participation in lawful off-campus activity, physical or mental disability, pregnancy, public assistance status, race, religion, sex, sexual orientation, spousal relationship to current employee, or veteran status, as applicable.

Veteran's Preference Notice:

This position is subject to North Dakota Veteran's Preference requirements.