Job Summary

Healthcare engineering is a trans-disciplinary field applying principles of engineering, science, and management to improve healthcare.  The successful candidate will facilitate trans-disciplinary collaboration necessary for the practical application of research to improve healthcare. 
Antimicrobial resistance (AMR) is a complex genetic phenomenon of microbial populations that results in extensive morbidity and mortality in human and animal populations. This position will support the Slizovskiy Lab, which focuses on advancing the knowledge of how AMR arises, persists, and spreads within and across microbiomes as well as host populations. The lab leverages development of novel metagenomic sequencing techniques, bioinformatic analyses, advanced algorithmics to process atypical data structures, and ‘big data’ visualization methods used in inferential analysis of AMR risk. Ultimately, these approaches are used to inform on practical interventions to mitigate or de-escalate the impacts of AMR within human and animal populations, ecosystems, and food.

The ideal candidate will be expected to identify, manage, and / or develop software tools for efficient data integration and for mining diverse types of biological data concerning bacterial genomes and their genes; collaborate closely with clinicians (i.e. veterinarians, physicians, and nurses) and scientists from a variety of disciplines, including microbiologists, epidemiologists, engineers, and computer scientists focusing on building innovative frameworks for data integration and automated data mining to resolve critical challenges of AMR in human health, animal health, and food safety; maintain and organize computing projects and data on HPC server space and implement best coding practices to optimize computational capabilities and workflows; work closely with end users to identify, analyze, and solve data challenges; maintain online repositories of publicly available software and data; and report progress in relation to objectives / critical milestones to funders and stakeholders. 

While it is expected that the individual in this position will assist in meeting data deliverables, they will have the opportunity to pursue independent research ideas. The incumbent is strongly encouraged to actively develop new project proposals in collaboration with Dr. Ilya Slizovskiy. The individual in this position will join the newest lab group focused on pathogenomics and metagenomics at Purdue University, with intention to grow into an established dynamic lab staffed with technical scientific staff undergraduate students, doctoral students, and post-doctoral scientists. Thus, strong interpersonal skills and the ability to collaborate as well as lead diverse research teams is a desired trait.

This position will be a hybrid role with required days on the WL campus so candidates must be able to commute daily, if needed.
 

Qualifications

Required:

• Master's degree in bioinformatics, computational biology, computer science, or related computational fields applied to microbial ecology and microbiology
• Two (2) years of related experience
• Experience with bash scripts/command line 
• Evidence of effective scientific writing, publication, or collaboration history
• Interdisciplinary collaboration skills, leading and participating as a team member

Preferred: 

• Expertise in analysis of metagenomic and metatranscriptomic datasets.
• Experience with integration of ‘omics datasets

Additional Information: 

• Purdue will not sponsor employment authorization for this position  
• A background check will be required for employment in this position
• FLSA: Exempt (Not Eligible For Overtime)
• Retirement Eligibility:  Defined Contribution Waiting Period
• Purdue University is an EOE/AA employer. All individuals, including minorities, women, individuals with disabilities, and veterans are encouraged to apply

Career Stream

Compensation Information:
Professional 3
Pay Band S0120
Job Code # 20003729