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Multimodal Learning Jobs in Tennessee (NOW HIRING)

GFT.

Bridge Project Engineer

Nashville, TN

$115K - $140K/yr

... multimodal roadway systems and ensure safe, efficient, and reliable travel for all. What you'll be ... learning, professional growth, and leadership development. In this capacity, the successful ...

GFT.

Bridge Project Engineer

Nashville, TN

$115K - $140K/yr

... multimodal roadway systems and ensure safe, efficient, and reliable travel for all. What you'll be ... learning, professional growth, and leadership development. In this capacity, the successful ...

GFT.

Bridge Project Engineer

Nashville, TN · On-site

$115K - $140K/yr

... multimodal roadway systems and ensure safe, efficient, and reliable travel for all. What you'll be ... learning, professional growth, and leadership development. In this capacity, the successful ...

Cisco

Applied AI Scientist

Memphis, TN · On-site

... multimodal machinegenerated data - including logs, time series, traces, and events. We combine deep ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

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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 as a Multimodal Learning Specialist, 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 Tennessee? For Multimodal Learning jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Multimodal Learning jobs in Tennessee look for? The top searched job categories for Multimodal Learning jobs in Tennessee are:
What cities in Tennessee are hiring for Multimodal Learning jobs? Cities in Tennessee with the most Multimodal Learning job openings:
Multimodal Learning jobs near you
Faculty Position - Department of Computational Biology

Faculty Position - Department of Computational Biology

St. Jude Children's Research Hospital

Memphis, TN • On-site

Full-time

Posted 16 days ago

St. Jude Children's Research Hospital rating

8.6

Company rating: 8.6 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

37th of 1,023 rated hospitals

Job description

SJCRH
Be the force behind the cures. From analyzing complex biomedical data in pediatric cancer to creating innovative technologies and computational tools, your work at St. Jude can directly impact patient care.
The Department of Computational Biology invites applications for Assistant/Associate/Full Member (faculty) positions in, but not limited to, the following areas:
A - Omics Technologies
We seek a computational technology innovator to develop next generation omics platforms for pediatric biology and diseases. Areas of interest include RNA biology (splicing/fusions), CRISPR functional genomics (CRISPRi/a, Perturb seq, combinatorial screens), single cell and spatial omics, metabolomics, and immunopeptidomics. The successful candidate will pioneer assay-algorithm co design, demonstrate cross disciplinary leadership and lead reproducible & open science
B - AI in Biomedicine
We seek an AI first scientist to build foundation models and agentic AI tools spanning DNA/RNA/protein/imaging/spatial/EHR data for pediatric precision medicine. Topics include regulatory genomics foundation models, multimodal tissue/cell models (WSI + spatial + single cell), proteogenomic/neoantigen prediction, computational pathology (WSI modeling, slide omics fusion) and clinical informatics, plus trustworthy AI (calibration, uncertainty, drift monitoring).
Candidates should have a record of impactful methods/tools, experience with clinical informatics pipelines, and a commitment to safe, reproducible, clinic ready AI.
Experience building LLM driven, agentic systems for automated analysis or experiment planning with appropriate guardrails is highly valued.
Environment & Resources
The Department occupies 28,700 square feet of laboratory and office space. Investigators have dedicated shared resources for large scale data analysis and functional validation, including priority access to cloud computing, a local high performance computing facility housed in a state of the art data center, a genomics laboratory for developing new omics technologies and assays, a wet lab supporting dry lab faculty, and an software engineering team for high throughput analysis and pipeline automation. The Department is key in multiple completed and ongoing institutional projects, including Pediatric Cancer Genome Project (PCGP), St. Jude Cloud, PedDep Accelerator, Real Time Clinical Genomics, iTARGETS, and COMET. The research environment at St. Jude is highly collaborative, with opportunities across basic and clinical departments and access to institution wide core facilities led by PhD level scientists.
Compensation & Appointment
We offer highly competitive packages, including generous startup funds, computing resources, equipment, laboratory space, personnel support, and potential institutional support beyond the start up phase.
Appointments at the Assistant or Associate or Full Member level will be considered.
Qualifications
• PhD (or equivalent) with at least three years of relevant postgraduate experience, or a demonstrated track record of developing novel, high impact computational methods.
• For Area A: evidence of assay-algorithm co design in RNA/CRISPR/single cell/spatial/metabolomics.
• For Area B: experience with foundation models, multimodal learning, clinical NLP, and trustworthy AI; familiarity with clinical informatics.
How to Apply
Please submit the following directly via the online application:
(1) a curriculum vitae, (2) a 2-3 page summary of research interests, and (3) the names of three references
For more information, contact ComputationalBiologyRecruitment@stjude.org.
St. Jude is an Equal Opportunity Employer
No Search Firms
St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude.

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