PhD Scholarship: Physical Machines: Join the Learning Machines group at AMOLF to delve into the realm of physical learning systems. This PhD position offers a unique opportunity to understand how simple building blocks give rise to complex behaviors in physical materials, bridging the gap between artificial and natural intelligence. If you’re passionate about unraveling the mysteries of learning in physical, biological, or computational systems, this scholarship is for you.
Study Area: Physics, Biophysics, Electrical/Mechanical Engineering, Materials Science, Computer Science
Location: AMOLF, Netherlands
Eligibility/Qualification: Candidates with a strong background in physics, biophysics, electrical/mechanical engineering, materials science, or computer science, with a keen interest in learning, are encouraged to apply. Excellent verbal and written communication skills in English are required. Proficiency in coding (Python/Matlab) and familiarity with numerical methods and machine learning concepts are advantageous. Candidates must meet the requirements for an MSc degree.
Description: The Learning Machines group at AMOLF, led by Dr. Menachem Stern, focuses on understanding the fundamental principles of learning in physical systems. Through analytical and computational modeling, and experimental exploration, researchers in this group seek to uncover the mechanisms behind physical learning and design new types of learning machines capable of solving complex engineering problems autonomously. Projects include the development of physically realizable learning rules inspired by biological systems and the study of the interplay between structure, interactions, and function in physical learning systems.
How to Apply: To apply, please submit your resume and a motivation letter (max. 1 page) explaining why you want to join the Learning Machines group. Applications will only be considered with a motivation letter included. Apply online via the provided button. For further inquiries, contact Dr. Menachem Stern, Group Leader (Learning Machines), at m.stern@amolf.nl or +31 (0)20-754 7100.
Last Date: Applications will be evaluated on a rolling basis, and positions will be filled as soon as an excellent match is found. Online screening may be part of the selection process. Apply soon to secure your chance to contribute to groundbreaking research in physical learning machines. AMOLF is dedicated to fostering an inclusive and diverse work environment, encouraging candidates from all backgrounds to apply. Commercial activities in response to this advertisement are not appreciated.
