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Computer Science Research Assistant Jobs in New Mexico

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Carlsbad, NM · On-site

Provide technical support to customers using Advanced BioMatrix products. * Assist customers with ... science research. * 3+ years of relevant experience in life science research, scientific ...

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Computer Science Research Assistant information

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$8

$21

$30

How much do computer science research assistant jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for computer science research assistant in New Mexico is $21.23, according to ZipRecruiter salary data. Most workers in this role earn between $17.93 and $24.71 per hour, depending on experience, location, and employer.

What is a computer science research assistant?

A Computer Science Research Assistant supports professors or researchers in conducting experiments, analyzing data, and developing software or algorithms. They may assist with literature reviews, coding, debugging, or testing to advance research projects. The role often involves working with programming languages, machine learning, cybersecurity, or other specialized topics. It provides valuable experience for students or professionals looking to deepen their knowledge in the field.

What are the typical duties and daily responsibilities of a computer science research assistant?

As a Computer Science Research Assistant, your daily responsibilities often include assisting with data collection and analysis, developing or testing software prototypes, conducting literature reviews, and helping prepare reports or presentations for research findings. You may also collaborate closely with faculty, graduate students, or industry partners, contributing to coding tasks, experimental setups, and troubleshooting technical issues as they arise. The role frequently involves both independent problem-solving and teamwork, providing a supportive environment to deepen technical skills and gain hands-on research experience. This position is an excellent opportunity to develop your research portfolio, learn advanced computing concepts, and prepare for potential graduate study or specialized tech careers.

What are the key skills and qualifications needed to thrive as a computer science research assistant?

To thrive as a Computer Science Research Assistant, you need strong analytical skills, proficiency in programming languages (such as Python, Java, or C++), and a solid understanding of algorithms and data structures, usually evidenced by coursework or a relevant degree. Familiarity with research tools, version control systems (like Git), and possibly certifications in data analysis or machine learning are advantageous. Excellent problem-solving abilities, attention to detail, and effective written and verbal communication help individuals excel when collaborating on research projects. These competencies enable you to support rigorous research, contribute to innovative solutions, and work efficiently as part of an academic or industry research team.

What are the most commonly searched types of Computer Science Research jobs in New Mexico?

The most popular types of Computer Science Research jobs in New Mexico are:

What are popular job titles related to Computer Science Research Assistant jobs in New Mexico?

For Computer Science Research Assistant jobs in New Mexico, the most frequently searched job titles are:

What job categories do people searching Computer Science Research Assistant jobs in New Mexico look for?

The top searched job categories for Computer Science Research Assistant jobs in New Mexico are:

Computer Science Research Assistant jobs near you
Infographic showing various Computer Science Research Assistant job openings in New Mexico as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $44,162 per year, or $21.2 per hour.

Research Asst Professor - Rudgers

Albuquerque, NM • On-site

University of New Mexico
See more jobs at University of New Mexicoexternal

Full-time

Posted 11 days ago

University Of New Mexico rating

8.2

Company rating: 8.2 out of 10

Based on 57 frontline employees who took The Breakroom Quiz

Job description

The Assistant Research Professor will conduct research under a National Science Foundation (NSF) Science and Technology for Advanced Research (STAR) award investigating the ecological and evolutionary mechanisms by which soil fungal microbiomes enhance plant adaptation to drought. The position will lead greenhouse, laboratory, and field studies examining plant-fungal interactions, microbiome diversity, and the mechanisms underlying microbe-mediated adaptation to environmental stress.

Primary responsibilities include experimental design and implementation; fungal isolation, culturing, inoculation, and trait characterization; plant propagation and physiological measurements; collection, management, and statistical analysis of ecological and microbiome data; preparation of manuscripts, technical reports, and grant deliverables; presentation of research findings at scientific meetings; mentoring undergraduate and community college student researchers; coordinating collaborations with faculty and external research partners; ensuring compliance with institutional and sponsor requirements; and assisting with the development of future externally funded research programs.

The Sevilleta LTER position will constitute a portion of the FTE but otherwise remain the same as the current responsibilities to work on projects of the Sevilleta Long-Term Ecological Research Program (LTER) as described in the proposal for the state of New Mexico Technology Enhancement Fund (0480YV). This work includes using the Terrestrial Ecosystem Model (TECO) together with field data and the plant traits database of the Sevilleta LTER program to project future dynamics of carbon. Work will also involve development of new publicly available interface for this project, EcoPAD - Modeling Platform for Dryland Forecasting. This involves technology enhancements that include automated data assimilation algorithms for wireless remote sensor data and phenocameras, data-driven modeling approaches to synthesize massive data, and software development.

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