Institution
University of Missouri
Location
Columbia, MO, USA
Close Date
Until filled

Description
The Libault lab is seeking a highly motivated and skilled Postdoctoral Research Fellow to join our dynamic research team focused on unraveling the intricate dynamics of plant-microbe interactions and evolution. The primary objective of this position is to develop a comprehensive understanding of the role of selected genes in these interactions and to contribute to the characterization of gene networks and modules. This will involve a combination of experimental work in the laboratory and computational analysis of molecular datasets.

Qualifications
The qualified candidate will have a PhD in the field of plant genomics, plant molecular biology, plant cellular biology, or plant biochemistry by start of the appointment.

Candidates will be evaluated on the following qualifications:

• Be a persuasive communicator with exceptional organizational and time management skills.
• Demonstrated durable skills of leadership, character, collaboration, communication, creativity, critical thinking, metacognition, mindfulness, growth mindset, and fortitude.
• Customer service, confidentiality, integrity, and a commitment to the University values.
• Available for reliable and consistent onsite work.

Company 
Bayer 
Location
Chesterfield, Missouri (St. Louis area).

Description 
Join a diverse team to innovate product discovery approaches that will help deliver Bayer’s vision of Health for All, Hunger for None. This role offers the opportunity to collaborate and network internally across functions and divisions as well as with external partners, both domestically and internationally. Success in this role means practicing world-class science to drive decisions and innovation, exemplifying inclusion and safety, development as a scientific leader, and embracing Bayer’s LIFE values: Leadership, Integrity, Flexibility, and Efficiency.

YOUR TASKS AND RESPONSIBILITIES
The primary responsibilities of this role, Spatial Transcriptomics Post Doctorate Histology & Microscopy (Crop Science), are: 

• Establishes/adapts and optimizes specimen preparation protocols for evaluating spatial transcriptomics technologies in plant, insect, and other tissues as relevant for Crop Science use cases;
• Designs experimental plans in collaboration with cross-functional, internal and external partners/stakeholders and coordinates implementation to enable project success;
• Understands and anticipates broader project/team interests, and identifies, proposes, and performs complementary analyses to enrich insights from multiple modalities/data layers;
• Demonstrates commitment to inclusion and safety and adherence to safety protocols and best practices;
• Maintains detailed record keeping and required documentation;
• Thrives on meeting challenges, insists on high quality deliverables, demonstrates effective problem-solving skills, and possesses the ability to build productive external and internal relationships;
• Conducts complex activities that require specialized knowledge and the broad application of scientific principles;
• Takes initiative in keeping current with literature in areas of  assignment and in areas of professional interest.

Required Qualifications:

• Ph.D. (defended by 1/30/24) in a biological, biomedical or similar field; 
• Strong experience with histology and confocal light microscopy including specimen preparation (such as fixing/embedding and sectioning). 

Preferred Qualifications:

• Three or more years of hands-on experience with histology and at least one specimen characterization technique (confocal microscopy, sequencing, etc.);
• Experience with agricultural specimens (plant, insect and/or fungi tissue);    
• Experience in imaging localization techniques (FISH or similar techniques);
• Background in image processing, quantitative image analysis, data visualization, and bioinformatics tools;   Familiarity with spatial transcriptomics or other innovative or emerging fields of study; 
• Basic understanding of genomic technologies/transcriptomics or next generation sequencing;
• Experience collaborating across multiple platforms/functions, fostering a team-based approach to research, and leveraging external partnerships to evaluate various spatial transcriptomics technology platforms;
• Experience clearly communicating project plans and experimental results with partners and stakeholders and maintaining alignment on objectives and priorities.

Institution
University of Missouri-Columbia
Location
Columbia, MO, USA
Apply By
Review of applications will begin November 15, 2023
Close Date
Until filled

Description
The University of Missouri-Columbia, Bond Life Sciences Center invites applications for a Post-Doctoral Research Associate position in plant single-cell computational biology in the Libault Lab. This 12-month (calendar year), full-time (1.0 FTE) special appointment will focus on expertise bioinformatics, computational biology, machine learning, and plant genomics. The apportionment is 100% research.

The Libault lab seeks to explore and understand the network of plant genes, and their regulation in response to environmental stress at the single-cell level. These discoveries will lead to the development of new strategies to improve crop characteristics and performance. Therefore, using single-cell-omics datasets, the postdoctoral associate will work on genomics, epigenetics, and/or bioinformatics/computational biology to characterize and functionally validate plant cell-type-specific transcriptional programs, their regulation in response to environmental stresses, and their differential regulation between plant species.

The selected postdoctoral researcher will have opportunities to work and develop computational resources to conduct:

• Comparative analyses of gene expression between crop species.
  
• Analyses of plant single-cell-omics datasets, integration of multiomic datasets, transcriptomic trajectories, and prediction of gene networks.
  

Qualifications
A PhD in plant computational biology/genomics, bioinformatics, machine learning, or a related field is required at the time of hiring.

Candidates will be evaluated on:

• Prior knowledge and demonstrated expertise in next-generation sequencing data analysis, computational systems biology, and/or data integration, excellent oral and written communication skills, and the ability to work in a team environment.
  
• A solid knowledge of plant genomics, evolution, gene expression/regulation, and biological networks.
  
• Persuasive communicators with exceptional organizational and time management skills.
  
• Durable skills such as leadership, character, collaboration, communication, creativity, critical thinking, metacognition, mindfulness, growth mindset, and fortitude as well as customer service, confidentiality, integrity, and a commitment to the University's values.
  

Institution
Bayer
Location
Chesterfield, Missouri, USA
Salary Range
$103,000/yr - $154,000/yr USD

Description
As part of a collaboration project focused on single cell sequencing for agricultural biology, we are seeking a passionate and creative scientist to join the Crop Science Plant Biotechnology genomics team. This two-year position will be focused on cutting edge functional genomics analysis to generate insights into the genome biology of various eukaryotic systems. This genomics data scientist will be part of an interdisciplinary team with expertise in Cell Biology, Genomics, and Data Science to move forward specific high-impact projects for crop science.

The ideal candidate will be able to implement cloud-based genomics pipelines and improve and optimize them; be able to generate insights from genomic data on various eukaryotic systems; work well with cross-disciplinary and global teams; and be able to articulate findings and integrate his/her work into the established R&D teams.

Responsibilities

• Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline.
• Independently perform statistical analysis, computer programming, predictive modeling and experimental design.
• Build cross-functional relationships to collaboratively partner with the business and effectively network amongst diverse scientific disciplines within data science, genomics, and biotechnology-based communities.
• Contribute to the development of code, bioinformatic analyses pipelines, computational tools, and databases for mining and visualizing large genomic data sets.
• Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact, and key performance indicators.
• Present compelling, validated stories to all levels of organization, including peers, senior management, and internal customers to drive both strategic and operational changes in business.

Qualifications

• PhD in a relevant domain, including genomics, computational biology, molecular biology & genetics, etc.
• Educational preparation or applied experience in at least one of the following areas: Machine Learning, Statistical Genetics, Statistics, Biostatistics, Bioinformatics, Genomics, Computational Biology or other related quantitative discipline.
• Demonstrates intermediate proficiency in computational skills and level of experience analyzing large datasets and/or building data models using R, Python or other statistical and/or mathematical programming packages.
• Demonstrates basic understanding of software development best practices (Version Control, Code Documentation & Review, Cloud Based Sequence Analysis, Database Management).
• Intermediate proficiency in machine learning algorithms and concepts.
• Experience in successful delivery of valuable analysis through application of domain knowledge.
• Strong communication competencies including presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner to extended team and small groups of key stakeholders.

Institution
Donald Danforth Plant Science Center
Location
Saint Louis, MO, USA

Description
The Baxter Lab and Mockler Lab at the Donald Danforth Plant Science Center are recruiting multiple enthusiastic postdoctoral associates to work on a DOE-funded project studying the response to abiotic stresses in Sorghum and Setaria. Candidates will work at the nexus of phenotyping, quantitative genetics, comparative genetics, and genomics. Large datasets from field and controlled environment phenotyping, genome sequencing, and transcriptomics are available, and more will be produced during the project.

Qualifications

• Ph.D. in a field related to plant bioinformatics (i.e., plant biology, quantitative genetics, bioinformatics, genomics, computer science, statistics) is required
• Experience in data science/bioinformatics, genomic data analysis, and statistical approaches

Institution
Donald Danforth Plant Science Center
Location
Saint Louis, MO, USA

Description
The laboratory of Dr. Christopher Topp at the Donald Danforth Plant Science Center is a plant root phenomics lab, innovating uses of 3D imaging modalities to investigate roots and their environment. We are seeking someone interested in plant biology and image analysis to help process and measure digitized plant samples using both external and internal analysis tools.

This involves maintaining a robust computational infrastructureincluding dedicated workstations, storage and processing servers, virtual machines, internal code bases, third-party software licenses, VR systems, cloud storage, databases, resource provisioning, development environments, and data management. This plays a key role in cutting-edge research and analytics to solve problems in agricultural sustainability. The role allows for creativity and autonomy in the design and development of tools and procedures. The successful candidate will be organized, independent, creative, have strong communication skills, desire to learn, and work with outstanding collaborative capability.

Qualifications

• B.Sc. in Computer Science, Computer Engineering, Information Technology or similar degree
• Strong understanding or exposure to Linux operating systems and familiarity with tools used to manage packages (dnf/yum/apt), compiling from source (i.e., Makefile), and routine system maintenance and troubleshooting
• Strong proficiency with Python and its ecosystem, including data science and scientific computing
• Strong proficiency in C/C++ (Exposure Go is acceptable)
• Familiar with relational databases (SQL)
• Familiar with version control systems (Git) and GitHub
• Comfortable working with custom file formats, both binary and plan text formats