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Multimodal Learning Jobs in Texas (NOW HIRING)

Abbott

Research Scientist II

Plano, TX

... multimodal datasets integrating biological, electrophysiological, imaging, and clinical data. * Partner with computational scientists, data scientists, and engineers to support machine learning and ...

Abbott

Research Scientist II

Plano, TX

... multimodal datasets integrating biological, electrophysiological, imaging, and clinical data. * Partner with computational scientists, data scientists, and engineers to support machine learning and ...

Abbott

Research Scientist II

Plano, TX · On-site

... multimodal datasets integrating biological, electrophysiological, imaging, and clinical data. * Partner with computational scientists, data scientists, and engineers to support machine learning and ...

KaashTech LLC

AI Architect

Dallas, TX · On-site

$62.50 - $82.50/hr

Deep knowledge of generative AI, agentic AI systems, and traditional machine learning, including ... Google Gemini for multimodal and large context window applications * Meta Llama and other open ...

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Multimodal Learning information

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.
What are popular job titles related to Multimodal Learning jobs in Texas? For Multimodal Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Multimodal Learning jobs in Texas look for? The top searched job categories for Multimodal Learning jobs in Texas are:
What cities in Texas are hiring for Multimodal Learning jobs? Cities in Texas with the most Multimodal Learning job openings:
Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Postdoctoral Fellow - GI Med Oncology - Research

MD Anderson Center

Houston, TX • On-site, Remote

$46K - $63K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 170 frontline employees who took The Breakroom Quiz

24th of 887 rated healthcare providers

Job description

The University of Texas MD Anderson Cancer Center seeks an outstanding Postdoctoral Fellow to join the Department of Gastrointestinal Medical Oncology in advancing foundational artificial intelligence (AI) models for oncology. This position is embedded within MD Anderson's Moon Shots Program, an institutional initiative aimed at accelerating scientific discovery and translational impact to significantly reduce cancer mortality. The successful candidate will contribute to the development of next-generation multimodal AI systems that integrate diverse clinical and biological datasets to improve patient outcomes, enhance clinical operation, and advance precision oncology.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
-Develop, refine, and validate foundational AI models using large-scale multimodal oncology datasets.
-Integrate heterogeneous data sources, including electronic health records, digital pathology images, radiology data, bulk and single-cell omics, and real-world clinical outcomes.
-Design and implement novel computational frameworks for therapy response modeling, treatment optimization, clinical trial matching, and patient care enhancement.
-Collaborate closely with clinicians, computational scientists, biologists, and disease groups across MD Anderson.
-Disseminate research findings through peer-reviewed publications and presentations at national and international scientific meetings.
-Assist in grant development and project coordination as needed.
ELIGIBILITY REQUIREMENTS
- PhD in Computer Science, Computational Biology, Bioinformatics, Electrical Engineering, Biomedical Engineering, or a related quantitative discipline.
- Demonstrated expertise in machine learning or deep learning, including familiarity with large language models, multimodal architectures, or generative AI.
- Proficiency in Python and modern machine learning frameworks (e.g., PyTorch, TensorFlow, JAX).
- Experience working with biological, clinical, or other high-dimensional datasets.
Preferred:
-Background in oncology, cancer biology, immunology, or translational research.
-Experience with foundational model development, self-supervised learning approaches, or large-scale distributed training.
-Familiarity with EHR data structures, digital pathology workflows, or multi-omics integration.
-Strong publication record demonstrating rigor, innovation, and independence.
ADDITIONAL APPLICATION INFORMATION
Access to one of the richest and most comprehensive cancer datasets worldwide, enabled by MD Anderson's status as the top-ranked cancer center with the nation's largest oncology patient volume.
• Integration into the Moon Shots Program, providing unique opportunities for high-impact translational research, cross-disciplinary collaboration, and accelerated clinical application.
• A highly collaborative and well-resourced environment with strong institutional support for AI, data science, and precision oncology initiatives.
• Competitive compensation and benefits in accordance with NIH and MD Anderson guidelines
POSITION INFORMATION
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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