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CI0082: Internship - Quantum AI
MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.
Responsibilities:
- Conduct cutting-edge research in quantum machine learning.
- Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
- Develop and implement algorithms using PyTorch and PennyLane.
- Publish research results at leading research venues.
Qualifications:
- Currently pursuing a PhD or a post-graduate researcher in a relevant field.
- Strong background and solid publication records in quantum computing, deep learning, and signal processing.
- Proficient programming skills in PyTorch and PennyLane are highly desirable.
What We Offer:
- An opportunity to work on groundbreaking research in a leading research lab.
- Collaboration with a team of experienced researchers.
- A stimulating and supportive work environment.
If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!
- Research Areas: Artificial Intelligence, Machine Learning, Signal Processing, Applied Physics
- Host: Toshi Koike-Akino
- Apply Now
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MS0102: Internship - Estimation and Calibration of Multi-physical Systems Using Experiments
MERL is looking for a highly motivated and qualified candidate to work on estimation and calibration of muti-physical systems governed by differential algebraic equations (DAEs). The research will involve study, development and efficient implementation of estimation/calibration approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited experimental data. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, optimization, and model calibration; with expertise demonstrated via, e.g., peer-reviewed publications. Prior experience in working with experimental data, and programming in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.
Required Specific Experience
- Graduate student with 2+ years of relevant research experience
Additional Desired Experience
- Strong programming skills in Julia or Modelica
- Prior experience in working with thermofluid systems
- Prior experience in estimation/calibration of complex nonlinear systems using experimental data
- Research Areas: Multi-Physical Modeling, Optimization, Control, Dynamical Systems, Applied Physics
- Host: Vedang Deshpande
- Apply Now