Research Assistant for Precision Apiculture Project

Position Overview

The Precision Apiculture Research Group, headed by Dr. Vladimir Kulyukin in the School of Computing at Utah State University, is seeking one or two hourly research assistants to support ongoing research in precision apiculture, biological image analysis, and computer vision.

The project focuses on the development, evaluation, calibration, and analysis of object detection models for dense hive frame imagery collected by USDA researchers. The successful candidate will work closely with image annotators and researchers involved in biological interpretation, dataset curation, machine learning experimentation, and confidence calibration for large-scale hive frame image datasets.

Responsibilities

 The research assistant will:

1. Design, train, tune, and evaluate object detection models for hive frame image analysis;

2. Collaborate closely with image annotators to analyze biologically meaningful structures in dense hive frame imagery;

3. Participate in annotation review, quality-control, and dataset refinement sessions;

4. Analyze model performance using confusion matrices, confidence distributions, and related statistical methods;

5. Assist with reproducibility, experimental organization, and technical documentation;

6. Maintain consistency and accuracy across model training and evaluation cycles.

Training in software tools, experimental workflows, and image annotation protocols will be provided on site.

Minimum Qualifications

1. Demonstrable experience with machine learning, deep learning, statistical analysis, or scientific computing;

2. Programming proficiency in Python;

3. Ability to work carefully and systematically with experimental data and model outputs;

4. Strong attention to detail and willingness to work in an interdisciplinary research environment;

5. Familiarity with Linux-based scientific computing environments. 

Preferred Qualifications

Preference will be given to applicants with one or more of the following:

1. Experience designing, training, and evaluating deep learning models for image analysis or object detection;

2. Background in computer science, mathematics, engineering, data science, or related fields;

3. Prior experience with biological image datasets or image annotation workflows;

4. Experience with Linux-based scientific computing environments;

5. Experience training deep learning models on GPU-equipped systems (e.g., CUDA-enabled environments);

6. Interest in precision apiculture, precision agriculture, biological imaging, or applied AI;

7. Practical beekeeping, entomology, or agricultural experience.

Hours: Up to 20 hours per week

Location: Logan, Utah 

Part-time Hourly

Fall 2026 and Spring 2027

Applicants should submit:

1. A detailed resume or CV;

2. A one-page cover letter describing:

   - relevant background and experience;

   - interest in the position;

   - how the position fits into the applicant’s academic or career goals.