Prior Labs
Research Scientist Intern (PhD)(m/w/x)
Full-timeInternshipOn-site
Berlin, Freiburg im Breisgau
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PRPrior Labs
Research Scientist(m/w/x)
Berlin
from 120,000 / year
Full-timeOn-siteExperienced
AI/ML
Data Science
Nejo AI Summary
Scaling transformer architectures for multimodal models on structured data, including time series. PhD in CS/related field, with top-tier publications or significant open-source contributions required. 30 days of paid vacation.
Requirements
- PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or related field
- Deep experience with ML frameworks, especially PyTorch and scikit-learn
- Strong engineering fundamentals with excellent Python expertise
- Experience in data science and working with tabular data or time series
- Publications at top-tier venues or significant open-source contributions
Tasks
- Develop new AI models
- Scale transformer architectures from 10K to 1M+ samples
- Build multimodal models for text and tabular data
- Create specialized architectures for time series and forecasting
- Implement efficient inference methods for production
- Research causal understanding in foundation models
- Design approaches for managing multiple related tables
Work Experience
- approx. 1 - 4 years
Education
- Doctoral / PhD
Languages
- English – Business Fluent
Tools & Technologies
- PyTorch
- scikit-learn
- Python
Benefits
More Vacation Days
- 30 days of paid vacation
Healthcare & Fitness
- Comprehensive healthcare
- Fitness benefits
Public Transport Subsidies
- Transportation benefits
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Prior Labs and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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PRPrior Labs
Research Scientist(m/w/x)
Berlin
from 120,000 / year
Full-timeOn-siteExperienced
AI/ML
Data Science
Nejo AI Summary
Scaling transformer architectures for multimodal models on structured data, including time series. PhD in CS/related field, with top-tier publications or significant open-source contributions required. 30 days of paid vacation.
Requirements
- PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or related field
- Deep experience with ML frameworks, especially PyTorch and scikit-learn
- Strong engineering fundamentals with excellent Python expertise
- Experience in data science and working with tabular data or time series
- Publications at top-tier venues or significant open-source contributions
Tasks
- Develop new AI models
- Scale transformer architectures from 10K to 1M+ samples
- Build multimodal models for text and tabular data
- Create specialized architectures for time series and forecasting
- Implement efficient inference methods for production
- Research causal understanding in foundation models
- Design approaches for managing multiple related tables
Work Experience
- approx. 1 - 4 years
Education
- Doctoral / PhD
Languages
- English – Business Fluent
Tools & Technologies
- PyTorch
- scikit-learn
- Python
Benefits
More Vacation Days
- 30 days of paid vacation
Healthcare & Fitness
- Comprehensive healthcare
- Fitness benefits
Public Transport Subsidies
- Transportation benefits
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Prior Labs and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Prior Labs
Industry
IT
Description
The company is building breakthrough foundation models that understand structured data, aiming to revolutionize scientific discovery and business intelligence.
Not a perfect match?
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