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Part Time Neural Networks Jobs (NOW HIRING)

Uottawa

APTPUO-Winter 2027-MEM5300 B (online)

Campus, IL

$239.47/hr

... neural networks, k-nearest neighbors, clustering, and association rules. Students will gain hands ... Professeur a temps-partiel regulier / Regular Part-Time Professor Date Posted (YYYY/MM/DD): 2026/05 ...

Barrow Wise Consulting

Researcher (PhD Student)

MD · On-site

$25/hr

... intelligence, neural networks, blockchain, and 5-D modeling An Ideal Candidate has: * Doctoral ... US Citizen * Job Type: Part-time-time Applicants selected for employment will be subject to a ...

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What is the difference between Part Time Neural Networks vs Part Time Data Analysts?

AspectPart Time Neural NetworksPart Time Data Analysts
Required CredentialsKnowledge of machine learning, programming, and neural network frameworksStatistical skills, data visualization, and basic programming
Work EnvironmentTech companies, research labs, or freelance projectsBusiness, finance, marketing, or healthcare sectors
Industry UsageAI development, research, and machine learning applicationsData interpretation, reporting, and decision support
Search & Comparison IntentUnderstanding roles involving neural network modelingAnalyzing data to inform business decisions

Part Time Neural Networks focus on developing and training neural network models, requiring technical skills in machine learning and programming. In contrast, Part Time Data Analysts interpret data sets to generate insights, often using statistical tools. Both roles are essential in data-driven industries but differ in technical complexity and application focus.

What are part-time neural networks jobs?

Part-time neural networks jobs involve working with artificial neural networks on a part-time basis, typically supporting tasks like data preprocessing, model training, evaluation, or deployment. These roles are common in research, startups, or companies needing flexible expertise in AI and machine learning. Responsibilities often include developing neural network models, analyzing datasets, and collaborating with teams to solve specific problems using deep learning techniques. Part-time positions allow professionals to balance other commitments while contributing to neural network projects.

What are some common challenges faced by part-time professionals working with neural networks, and how can they overcome them?

Part-time neural network professionals often encounter the challenge of staying up-to-date with rapidly evolving technologies and best practices while managing limited hours. Additionally, collaborating with full-time team members can require effective communication to ensure continuity on projects. To overcome these challenges, it's helpful to set clear expectations with your team, use project management tools to track progress, and allocate time each week to learn about the latest advancements in neural networks. Regular check-ins and documentation can also help bridge any gaps that may arise due to part-time scheduling.

What are the key skills and qualifications needed to thrive as a Neural Networks Engineer, and why are they important?

To thrive as a Neural Networks Engineer, you need a strong foundation in mathematics, programming (especially Python), and deep learning concepts, usually supported by a degree in computer science or a related field. Familiarity with tools and frameworks like TensorFlow, PyTorch, and Keras, as well as experience with cloud platforms, is typically required. Problem-solving abilities, attention to detail, and strong communication skills help set top professionals apart in this field. These skills are crucial for designing, implementing, and optimizing neural network models that drive effective AI solutions.
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What job categories do people searching Part Time Neural Networks jobs look for? The top searched job categories for Part Time Neural Networks jobs are:
Part Time Neural Networks jobs near you
Infographic showing various Part Time Neural Networks job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 59% Full Time, and 40% Part Time. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.
Adjunct Lecturer, Applied Deep Learning and AI (On-Campus, Fall '26)

Adjunct Lecturer, Applied Deep Learning and AI (On-Campus, Fall '26)

Columbia University

New York, NY • On-site

$11K - $13K/mo

Part-time

Posted 11 days ago

Job description

Company Description

Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society.

The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.

Job Description

The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community.

Responsibilities

  • Lead class lectures, instructional activities, and classroom discussions. Attend all class sessions.

  • Monitor and address student concerns and inquiries.

  • Evaluate and grade student work and assessments.

  • Conduct office hours.

Qualifications

Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training and a record of accomplishment as practitioners in an applied industry setting. 

Requirements

  • Doctorate degree in Computer Science, Data Science, or a related field.
  • Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch).
  • Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques.
  • Hands-on experience with deep learning projects and data manipulation.

Preferred Skills & Experience

  • 10+ years of related professional experience. 

  • 2+ years of University teaching experience, ideally at the graduate level.

  • Strong verbal and written skills for explaining concepts clearly.

Additional Information

Salary range: $11,000 - $13,000 per semester long course
Please submit a resume inclusive of university teaching experience.

All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity Employer / Disability / Veteran

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