PhD-student: Physical learning machines

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The Learning Machines group seeks motivated PhD students to join our team working on learning in physical systems. What are learning machines? Imagine your favorite artificial intelligence \ machine learning system figuring out how to solve a task, possibly classifying images of cats and dogs; now imagine a physical material doing the same thing – without any computer involved! In our group, we strive to understand the fundamental principles of learning in the real world; how simple building blocks give rise to emergent desired complex behaviors and functionalities. Such learning machines blur the lines between inanimate and living systems, between materials and computers, and redefine our understanding of natural and artificial intelligence. We use modelling and experimental data to develop and understand ways in which such physical learning is realized, and design new types of learning machines capable of solving complex engineering problems on their own. Some examples include neuromorphic computers (physical machine learning algorithms) and novel meta-materials with unique properties.

We offer PhD positions that are focused on the theoretical understanding of physical learning and physically realizable learning rules. The project will involve analytical and computational modelling of physically and biologically inspired systems, and the development and characterization of physical methods by which such systems can learn.

The aim of one project is to develop and analyze new methods by which diverse dynamical systems are able to learn. Our goal is to connect these ideas to real-life biological learning systems, such as slime molds (e.g. Physarum Polycephalum, living fluidic networks) as well as systems in our own bodies (immune  and vascular systems). Such systems exist in complex changing environments and must adapt (learn) to survive. We would like to know how the simple learning rules implemented by such systems gives rise to diverse and intricate behaviors in nature. Can we mimic this behavior to create novel synthetic materials?

The aim of another project is to study the interplay of structure, interactions and function in physical learning systems. We aspire to understand how structure and topology develops in learning systems, as well as how such structures hint at the functions these systems learn to perform. The goal of this project is to understand why actual learning systems look like they do. Why are certain structural motifs (hierarchies and gating structures) so common in the brain and machine learning algorithms? How does learning itself gives rise to such networks?

About the group

The Learning Machines group is a new group at AMOLF, led by Dr. Menachem Stern. It focuses on the development of fundamental understanding and theories regarding learning, from a physical perspective, under real world constraints. The group aims to bridge knowledge gaps between computational and biological learning, as well as to design physical learning machines that autonomously solve hard inverse design problems. For an introduction to this new and exciting field, see Stern and Murugan, Ann. Rev. Cond. Mat. Phys (2023).

Our group members work closely together with extensive support from the group leader and AMOLF resources in all aspects of design, realization, and interpretation of computational models of physical learning. Within the group as well as among the different groups at AMOLF, we have a strong focus on stimulating development of students in all professional aspects, as well as collaborations with other researchers at AMOLF and beyond. Moreover, we work closely together with international groups and companies.  For more information, see Group Learning-machines


We seek candidates with a strong background in physics, biophysics, electrical\mechanical engineering, materials science, or computer science with an interest in learning, broadly defined, in physical, biological or computational systems. Excellent candidates with training in any area of science or engineering will be considered. PhD candidates must meet the requirements for an MSc degree. Good verbal and written communication skills in English are required. Other advantageous qualities include experience with coding (Python\Matlab) and numerical methods, as well as familiarity with concepts in machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and welcome applicants with any background.

Terms of employment

The position is intended as full-time (40 hours / week, 12 months / year) appointment in the service of the Netherlands Foundation of Scientific Research Institutes (NWO-I) for the duration of four years, with a starting salary of gross €2,781 per month and a range of employment benefits. After successful completion of the PhD research a PhD degree will be granted. Several courses are offered, specially developed for PhD-students. AMOLF assists any new foreign PhD-student with housing and visa applications and compensates their transport costs and furnishing expenses.

Contact info

Dr. Menachem Stern
Group leader (Learning Machines)
Phone: +31 (0)20-754 7100

You can respond to this vacancy online via the button below.
Please annex your:
–  Resume;
–  Motivation on why you want to join the group (max. 1 page).

It is important to us to know why you want to join our team. This means that we will only consider your application if it entails your motivation letter.

Applications will be evaluated on a rolling basis and as soon as an excellent match is made, the position will be filled.

Online screening may be part of the selection.

AMOLF is highly committed to an inclusive and diverse work environment. Hence, we greatly encourage candidates from any personal background and perspective to apply.

Commercial activities in response to this ad are not appreciated.

Job details

PhD-student: Physical learning machines
Science Park 104 Amsterdam, Netherlands
Application deadline
Job type
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About the employer

AMOLF carries out fundamental physics with an open eye for applications. The researchers work closely with universities, institutes, and companies.

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