Computational Biology Gene Expression Co-Op

Computational Biology Gene Expression Co-Op We are seeking a motivated Computational Biology Co‑Op to support the development of next‑generation gene expression design capabilities. This role will focus on building and analyzing large‑scale coding and non‑coding DNA sequence datasets, evaluating gene expression prediction technologies, and applying machine learning approaches to improve expression element design across crop species. The successful candidate will work with computational biologists, genomics scientists, and gene design researchers to generate datasets, benchmark predictive models, and identify biological patterns that influence gene expression.YOUR TASKS AND RESPONSIBILITIESThe primary responsibilities of this role include:Curate, integrate, and analyze coding and non‑coding sequence datasets;Develop datasets suitable for training and evaluating machine learning models for gene expression prediction and design;Identify crop‑specific sequence patterns associated with expression outcomes across diverse datasets;Evaluate current computational tools and technologies used to annotate, score, and predict the function of coding and non‑coding DNA sequences;Develop reproducible analysis workflows and document data processing methodologies;Create visualizations and summaries to communicate biological and computational insights to research teams;Present project findings and recommendations to multidisciplinary stakeholders.Potential Project Areas:Benchmarking gene expression prediction and annotation technologies;Developing data‑driven approaches to improve expression element selection and optimization.WHO YOU AREBayer seeks an incumbent who possesses the following:Desired Qualifications:Pursuing a Master’s or Ph.D. degree in Bioinformatics, Computational Biology, Genetics, Genomics, Data Science, Computer Science, Plant Biology, or a related field;Experience with data analysis using Python and/or R;Familiarity with genomics, transcriptomics, machine learning, or statistical modeling;Interest in gene regulation, regulatory genomics, AI applications in biology, and genetic design;Experience working with biological sequence data is preferred;Strong analytical thinking, scientific communication, and problem‑solving skills;Ability to work independently and collaboratively in a multidisciplinary research environment.Employees can expect to be paid a salary of approximately between $22.75 to $47.75. Additional compensation may include a bonus or commission (if relevant). Additional benefits may include health care, vision, dental, retirement, PTO, sick leave, etc (if relevant). This salary (or salary range) is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.This posting will be available for application until at least March 19, 2027YOUR APPLICATIONBayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer. To all recruitment agencies: Bayer does not accept unsolicited third party resumes. Bayer is an Equal Opportunity Employer/Disabled/Veterans Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below. Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.Bayer is an E-Verify Employer.Location:United States : Missouri : Chesterfield Division:Crop Science Reference Code:883388 Contact UsEmail:View email address on click.appcast.io