INTERN - Quantum and Machine Learning engineer
il y a 6 jours
Job Title
Trainee - Quantum MLContext:
Deep Learning (DL) is transforming industries and shaping daily life. However, training and deploying advanced models require substantial computational power, leading to unsustainable energy consumption. As DL models grow in complexity and scale, improving their energy efficiency has become a critical challenge.
Project Objective:
Quantum technologies have the potential to provide an energy advantage for Machine Learning, for example by overcoming the von Neumann bottleneck through quantum neuromorphic computing or by leveraging quantum superposition to train classical binary neural networks.
This internship is part of an ongoing Quantum Machine Learning project, aiming to:
Explore existing quantum machine learning methods and assess their potential for energy savings
Develop new approaches, with a special focus on Bayesian methods that naturally provide robust uncertainty estimates
We are looking for principled Bayesian machine learning methods that could be implemented in a quantum or hybrid setup—such as quantum neural networks equivalent to Gaussian Processes or quantum hypernetworks defining a posterior over the parameters of classical binary networks—possibly drawing from modern Bayesian techniques.
Profile:
Master's student (Computer Science, Applied Mathematics, AI, Quantum Physics)
Strong interest in fundamental research and Bayesian approaches
Knowledge of Machine Learning and/or Quantum Computing is a plus
Diversity & Inclusion
Amadeus is an Equal Employment Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or a related medical condition), ancestry, national origin, age, genetic information, military or veterans status, sexual orientation, gender expression, perception, or identity, marital status, mental or physical disability status, or any other protected federal, state, or local status unrelated to performance of work involved.
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