Candidates must be United States Citizens, U.S. Nationals, or U.S. Permanent Residents to apply to the NSF I-PERF program. Candidates must apply to this position through the IPERF portal : https : / / www.i-perf.org / # / Schedule : Flexible Monday through Friday. Candidates may be in-person or hybrid. Location : Candidates based in Baltimore, MD or willing to relocate to Baltimore, MD are strongly preferred. Candidates must be able to be in the office an averaged minimum of once per week. What will you do? You will work as a part of our computer vision team to develop the algorithms central to our automated mosquito surveillance products. These products support public health activities in preventing vector borne disease by reducing capacity and capability barriers required to gather vector surveillance data. The most central activity is developing high accuracy fine grained classification algorithms, but various other computer vision tasks such as image processing and object detection are also required to support product function. Previous experience in computer vision, deep learning, and / or image processing through projects, coursework, internships, or employment is expected. Vectech is a startup and responsibilities are dynamic across several exciting, cutting-edge projects. You will have the opportunity to learn new roles, and take on additional responsibilities in our growing team. Responsibilities may include : Primary iterative development of IDX species identification algorithms for operational use Confirming model improvements through validation experiments on the wider dataset Translating improvements to operational use through development of models to be deployed in the IDX mosquito species identification system Improving and performing data cleaning and screening human-in-the-loop algorithms, such as ORSAC, to inform the entomology team’s label review efforts Perform scientifically sound testing, with appropriate quality metrics, and when necessary, developing custom metrics or methods for analysis. Identifying and developing new methods using literature review and technical blogs to address specific computer vision and deep learning problems Collaborating on writing publications on novel work by the team Following repository management protocols for working in collaboration Collaborating with the software engineering team for deployment of models and methods in the cloud and on the edge Collaborating with Computer Vision and Product team members on retrospective analysis to inform model development Working on cloud and local deep learning computers / instances Documenting experimentation and development Desired Knowledge