AI-assisted discovery of chiral perovskites in structured photonic environments

Work Activities
AI-assisted discovery of chiral perovskite (quantum) emitters. You will leverage an AI-assisted materials optimization robot being developed at AMOLF as a route to rapid, closed-loop synthesis and characterization across large perovskite parameter spaces. You will optimize directly for figures of merit such as chirality, emission wavelength, brightness, and stability. Because optical excitation of perovskites can move atoms around, sculpting the optical field in time and space can enable programmable optical functions. By conducting approximately 1 million optical experiments in parallel using a spatial light modulator and hyperspectral camera, you will explore chemical and processing landscapes in days that would otherwise take a student a lifetime to cover systematically. Special attention will be paid to 2D and related low-dimensional perovskites, with the aim to understand if intrinsically strong material chirality is a necessary condition for reaching strong programmable chirality.

Materials discovery in non-trivial optical environments. Building on the first thrust, you will introduce a new paradigm in optoelectronic device design: to synthesize and screen perovskite emitters directly on substrates that already provide a structured optical response. In a material that can be sculpted by the optical field, this provides the opportunity for a self-optimizing response – the local optical field imprints a symmetry on the material, which then amplifies the light-matter coupling. In the conventional workflow, one first finds a material with good intrinsic properties and only then designs the surrounding cavity, resonance, or metasurface. You will reverse that order, replacing an optically inert glass slide by a substrate with a designed resonance arising from refractive-index structuring (optical metasurface). Thin perovskite films spun onto such a substrate will respond most strongly when their emission (absorption) spectrum aligns with the photonic resonance. Likewise, if the figure of merit is chirality and the substrate itself has chiral optical response, one can search for materials whose intrinsic emission (absorption) couples most effectively to that environment. In this way, the optical structure of the substrate transforms the material on top into exactly what it needs for maximal performance. Such exploration offers tantalizing opportunities to study the impact of local versus nonlocal optical resonances in the context of material optimization. How does the spatial extent of the photonic resonance affect the material symmetry? Does the optical field profile induce spatially varying chiral properties in the perovskite? Understanding the coupling between photonic environment and material crystallization poses completely new opportunities in material synthesis for advanced photonic integration and technologies.

Qualifications
You have a Msc degree in physics, chemistry, materials science, engineering or another scientific discipline. You have an analytical mind with the ability to think about a problem and come up with logical and creative solutions. You can work independently and also enjoy collaborating in a team. You enjoy actively engaging others to learn new concepts and experimental methods. You are eager to give and receive feedback and see it as an opportunity for personal and professional growth. You have excellent communication skills in English. You are responsible, reliable and dedicated while caring about the well-being of yourself and your colleagues. You value and cherish diversity of background, opinion and ability and see a team as more than the sum of its parts.

Work environment
This is a collaborative project between AMOLF and the University of Amsterdam. Your contract and host group will be the Nanoscale Solar Cells group led by Erik Garnett at AMOLF amolf.nl/research-groups/nanoscale-solar-cells but you will also work part time at the Van der Waals-Zeeman institute of the University of Amsterdam iop.uva.nl/wzi/wzi.html with professors Sander Mann www.mannlab.nl and Jorik van de Groep vandegroeplab.com in the department of physics. These institutes are located next to each other in the Amsterdam Science Park. This collaborative position opens up many experimental and theoretical tools and facilities needed for the project including the AI-assisted automated synthesis and characterization robot, the AMOLF nanolab www.amolf.nl/nanolab, ultrafast, chiral and single photon microscopes and full-wave numerical simulations and analytical models. We share a vibrant, inclusive and collaborative work culture that welcomes scientists and engineers with diverse backgrounds and skills. There is a very active staff association organizing regular social activities throughout the year to enrich the work experience and community. There is excellent support staff to help with the more technical and administrative parts of the project.

Working conditions

• The working atmosphere at the institute is largely determined by young, enthusiastic, mostly foreign employees. Communication is informal and runs through short lines of communication.
• 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
• The starting salary is 3.115 Euro’s gross per month and a range of employment benefits.
• After successful completion of the PhD research a PhD degree will be granted at a Dutch University.
• 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.

More information?
For further information about the position or specific questions, please contact Erik Garnett: e.garnett@amolf.nl

Application
You can respond to this vacancy online via the button below. Please apply by submitting a CV and cover letter explaining why you want this specific PhD position, what you can contribute and what you would like to learn. Applications will be evaluated beginning on June 15th and will continue until the position is filled.

Online screening may be part of the selection.

Diversity code
AMOLF is highly committed to an inclusive and diverse work environment: we want to develop talent and creativity by bringing together people from different backgrounds and cultures. We recruit and select on the basis of competencies and talents. We strongly encourage anyone with the right qualifications to apply for the vacancy, regardless of age, gender, origin, sexual orientation or physical ability.

AMOLF has won the NNV Diversity Award 2022, which is awarded every two years by the Netherlands Physical Society for demonstrating the most successful implementation of equality, diversity and inclusion (EDI).

Commercial activities in response to this ad are not appreciated.

PhD position to model the climate of Antarctica until 2300

The future mass loss of the Antarctic ice sheet constitutes the greatest uncertainty in projections of global sea level rise. A major part of this uncertainty resides in the timing of ice flow acceleration, in response to ice shelf collapse resulting from surface meltwater ponding and hydrofracturing, as happened to the Larsen B ice shelf in 2002 and numerous ice shelves before and since. In this project, we will quantify when, where, and under what warming scenario this process will become critical for other Antarctic ice shelves. Unique aspects of this work are that we will look at three climate scenarios (low/mid/high warming), extend the projection horizon to 2300, and use unprecedented high resolution. To that end, you will work with two state-of-the-art climate models: the regional climate model RACMO2.4 (forced at the lateral and top boundaries by the earth system model IPSL and/or CESM) and IMAU-FDM which simulates the firn layer, which is currently being updated with a new hydrology routine to allow melt ponding to be predicted.

This project introduces you to the cryospheric research community and enables you to become an expert in the future of the Antarctic ice sheet. The research project is a blend of modelling and data analysis, in which you will run long simulations and analyse and visualize the large datasets that the models produce. In doing so, your work will help to constrain — or warn for — future sea level rise. You will be part of a team of ~20 researchers, including fellow PhD candidates and postdoctoral researchers working with RACMO2.4 and IMAU-FDM. During your PhD, you will attend international scientific conferences to share your research with the wider community. Besides the research work, you will assist in academic courses in the Department of Physics.

PhD Position Long-Term Reasoning and Adaptive Learning for Human-Aware Robot Autonomy

Job description
Autonomous robots working in human-centered environments must do more than react to immediate sensor input. They need to reason over longer time horizons, adapt to changing tasks and environmental conditions, and update their behavior when new observations become available. In the EU-funded OPERA project, TU Delft contributes to General-Purpose AI for robotics by developing methods that combine fast System 1-style behavior with more deliberate System 2-style reasoning, adaptation, and decision-making.

In this PhD position, you will develop methods for long-term reasoning and adaptive robot behavior in human-centered environments. Your research will focus on how robots can use learned models, memory, semantic information, task structure, and uncertainty estimates to make robust decisions over extended time horizons. This directly connects to OPERA’s task on long-term reasoning in human-centered environments, which combines adaptive learning, hierarchical reinforcement learning, semantic maps, predictive control, and deliberative planning to support long-horizon mobile manipulation and human-centered autonomy.

This project will also address learning for adaptive and robust robot interaction. You will investigate how robots can adapt their behavior in response to human proximity, predicted intent, task context, environmental change, uncertainty, or model mismatch. This may involve reinforcement learning, imitation learning, adaptive control, model learning, state estimation, semantic reasoning, or self-supervised learning. The focus is on enabling robots to remain safe and effective when operating conditions change, rather than learning policies that only work in a fixed training distribution.

You will work in the Cognitive Robotics Department at TU Delft under the supervision of Prof. Robert Babuška and Dr. Laura Ferranti. You will be embedded in the Reliable Robot Control Lab and contribute to TU Delft’s OPERA work on reliable, adaptive, and trustworthy robot autonomy.

Job requirements
The ideal candidate for this PhD position has a strong technical background and is enthusiastic about contributing to safe, intelligent, and adaptive robot autonomy. We welcome applicants from all backgrounds who are motivated to work at the intersection of long term reasoning, learning, and human aware robotic behaviour.

You have:

• A MSc degree in Systems and Control, Computer Science, Applied Mathematics, Robotics, Mechanical Engineering, Artificial Intelligence, or a closely related field.
• A strong interest in working across multiple research domains, including task level reasoning, control, perception, and machine learning.
• Excellent programming skills, particularly in Python and/or C++, and experience with modern software development tools.
• A passion for ground breaking theoretical research combined with an eagerness to test ideas on real robotic systems.
• Strong analytical and mathematical abilities, enabling you to work confidently with algorithms, optimization, probability, or learning frameworks.
• Excellent communication skills and proficiency in English (written and verbal), as required for academic publication and international collaboration.

You are particularly encouraged to apply if you have experience in one or more of the following areas:

• Reinforcement learning, model-based RL, hierarchical RL, imitation learning.
• Adaptive control, learning-based control, nonlinear system identification, state estimation.
• Long-horizon planning, semantic reasoning, memory-based decision-making.
• Human-aware robot behaviour, multi-agent interaction, adaptive interaction strategies.
• Continual/self-supervised learning, uncertainty estimation, domain adaptation.
• Real robot deployment and ROS/ROS2 as a strong plus.

As part of OPERA, you will travel to meet and collaborate with the project’s European partners and attend regular consortium meetings.

We particularly encourage applications from women and other underrepresented groups, as we are committed to building a diverse and inclusive research environment.    

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Mechanical Engineering
From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

PhD Position Safe GPAI Planning, Decision-Making and Active Perception for Reliable Robot Autonomy

Job description
Robots operating in dynamic, real-world environments must make reliable decisions despite uncertainty, incomplete perception, changing conditions, and the presence of people. In the EU-funded OPERA project, TU Delft contributes to the development of General-Purpose AI for robotics by combining fast reactive behavior with more deliberate reasoning, planning, and uncertainty-aware decision-making.

In this PhD position, you will develop methods for safe GPAI-powered planning and decision-making for autonomous robots. Your research will focus on the interface between model-based control, learning-based decision-making, and active perception. You will investigate how robots can decide when to act reactively, when to plan over longer horizons, and when to actively gather additional information before executing a task. This directly supports OPERA’s work on GPAI-powered planning and decision-making in complex and dynamic environments, where robots must combine hybrid data-driven and physics-based architectures, safe navigation envelopes, contingency planning, and active perception.

A central part of the project will be active perception for reliable autonomy. You will study how robots can select informative viewpoints, fuse multimodal sensor data, estimate uncertainty, and adapt their safety margins or plans when perception is incomplete or ambiguous. This connects to OPERA’s work on uncertainty-aware and safe active environment perception, where uncertainty estimates guide safety heatmaps, adaptive sensing, and safety-aware navigation and manipulation.

The core of the research will be safe planning and decision-making under uncertainty, with active perception as a mechanism for reducing uncertainty before or during task execution. Depending on your background, this may involve model predictive control, trajectory optimization, uncertainty-aware planning, sensor fusion, viewpoint selection, contingency planning, or selected elements of safe learning.

You will collaborate with OPERA partners developing perception, world models, simulation tools, and GPAI components, and your work will contribute to the OPERA open-source toolbox.

You will work in the Cognitive Robotics Department at TU Delft under the supervision of Prof. Robert Babuška and Dr. Laura Ferranti. You will be embedded in the Reliable Robot Control Lab and contribute to TU Delft’s OPERA work on reliable, adaptive, and trustworthy robot autonomy.

Job requirements
The ideal candidate for this PhD position has a strong technical background and is enthusiastic about contributing to safe, intelligent, and trustworthy robot autonomy. We welcome applicants from all backgrounds who are motivated to work at the intersection of planning, learning, and perception.

You have:

• A MSc degree in Systems and Control, Computer Science, Applied Mathematics, Robotics, Mechanical Engineering, Artificial Intelligence, or a closely related field.
• A strong interest in working across multiple research domains, including motion planning, control, perception, and machine learning.
• Excellent programming skills, particularly in Python and/or C++, and experience with modern software development tools.
• A passion for ground breaking theoretical research combined with an eagerness to test ideas on real robotic systems.
• Strong analytical and mathematical abilities, enabling you to work confidently with algorithms, optimization, probability, or learning frameworks.
• Excellent communication skills and proficiency in English (written and verbal), as required for academic publication and international collaboration.

You are particularly encouraged to apply if you have experience in one or more of the following areas:

• MPC, optimal control, trajectory optimisation, motion planning.
• Safe/robust control and decision-making under uncertainty.
• Active perception, sensor fusion, uncertainty estimation, viewpoint planning.
• Reinforcement learning or safe learning as useful, but not the main identity.
• ROS/ROS2, simulation and real robotic validation.

As part of this OPERA, you will be requested to travel to visit the different partners in the consortium and attend the regular project meetings.

We particularly encourage applications from women and other underrepresented groups, as we are committed to building a diverse and inclusive research environment.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Mechanical Engineering
From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

Postdoc Cyclic Programming and Reasoning

Job description

Software is at the core of modern society — from communication networks and financial systems to medical devices and transport infrastructure — and ensuring that it behaves correctly is both essential and notoriously difficult. Proof assistants such as Agda and Rocq (formerly Coq) make it possible to construct mathematically rigorous, machine-checked guarantees about software behaviour, but applying them to programs written in mainstream languages remains a significant challenge. This is especially true for software that exhibits cyclic behaviour: programs with loops, recursive data, or continuous interaction with their environment, which require a careful interplay of inductive and coinductive reasoning to verify.

In this postdoc position, you will work at the intersection of proof assistants and modern systems programming. Your central task is to design and prototype a way to verify Rust programs — and in particular programs with cyclic structures — by translating them, together with logical annotations supplied by the developer, into a proof assistant where their correctness can be machine-checked. The aim is not to build yet another verification tool from scratch, but to make state-of-the-art research on inductive-coinductive type theory genuinely usable for Rust developers. You will work closely with a parallel PhD project on first-class coinduction in proof assistants, helping to refine the underlying type theory and putting it to the test on realistic Rust programs.

This position is part of the NWO-XL consortium project Cyclic Structures in Programs and Proofs: New Harmonies in Software Correctness by Construction, a collaboration between five Dutch universities (TU Delft, Groningen, Leiden, Nijmegen, and Twente) which brings together expertise in formal logic, programming language theory, concurrency, and proof assistants. You will be based at TU Delft in the Programming Languages group, supervised by Jesper Cockx, and will collaborate closely with the other PhD students, postdocs, and senior researchers in the consortium. Within the wider project, your work forms a bridge between foundational research on coinductive reasoning and its practical application to real programs, and as such will play a key role in demonstrating that the consortium's theoretical advances translate into concrete tools that practitioners can use.

You will have significant freedom to shape the technical agenda, publish your findings at leading venues (such as POPL, ICFP, OOPSLA, ITP, and CPP), and contribute to the open-source tools developed within the consortium. You will also be encouraged to spend time at one of the partner universities and to engage with the broader national and international research community via the NetTCS network and consortium-organised workshops and schools.   

Job requirements

• A PhD in computer science, mathematics, or a closely related discipline (obtained or expected to be obtained before the starting date)
• Solid experience using a proof assistant such as Agda, Rocq, or Lean, ideally for non-trivial formalisations or for research on the proof assistant itself
• A strong background in type theory and/or programming language theory, including familiarity with topics such as dependent types, type systems for program verification, or operational/denotational semantics
• The ability to conduct independent research, demonstrated by peer-reviewed publications at relevant international venues
• Good written and spoken English, and the communication skills needed to collaborate effectively within a multi-site consortium   

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.