AI Researcher: Scaling Deep Learning for Real-World Optimization
2 dagen geleden
Job Description
At ORTEC, we tackle thousands of optimization challenges daily. While each problem is unique, patterns emerge in the problem instances we solve. Can we harness these patterns to learn from previously optimized solutions?
The Challenge
We've made progress in using deep learning models to predict solution structures for combinatorial optimization problems, but they're limited to simpler, moderate-sized problems. We aim to scale up these techniques to larger, more realistic problem instances, such as real-world routing problems with thousands of customers.
What You'll Do
- You'll develop efficient deep learning training pipelines that can run advanced neural network architectures like transformers on GPU.
- You'll work with master students in AI or related fields and have practical experience with training deep learning models on hardware accelerators using frameworks like PyTorch, TensorFlow, or JAX.
- You'll be comfortable in Python and familiar with concepts like vectorized programming, threading, etc.
- Experience with graph neural networks/transformers is a plus.
- You'll communicate fluently in English, verbally and in writing.
- You'll be available for 6 months, working 32-40 hours a week.
What We Offer
- A chance to work with a passionate team dedicated to applying mathematics to improve our planet.
- Inclusion in a company founded by graduate students in 1981, which still fosters its students through excellent supervision, good internship allowance, laptops, and other office facilities.
- An open, kind, and fun culture.
- Young ORTEC, an association that organizes activities for juniors, mediors, and graduate students at ORTEC, including Thursday afternoon drinks at our own bar "ORTap".
Estimated Salary Range: €45,000 - €60,000 per year (dependent on qualifications and location)
How to Apply: Upload your CV, motivation letter, and grade lists (BSc and MSc in one PDF if applicable). The recruitment process will consist of two online assessments, a first interview, and a second interview with a case.
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