Postdoc: Machine learning for wind flow prediction in coastal dunes

In this 2-year postdoc position, you will use numerical modelling and machine learning techniques to increase the accuracy of coastal dune models. As a result, your project will inform and enhance decision-making in coastal dune management.

The ultimate goal of this project is to build a surrogate wind field model that can feed accurate wind field predictions into numerical coastal dune models. You will start by evaluating current wind field predictions from the coastal dune model AeoLiS by comparing model output with existing field measurements and/or Computational Fluid Dynamics (CFD) simulations. Specifically, you will assess where simplified assumptions in the model fall short. You will then train a machine learning model (such as PySR or neural networks) on CFD data to develop a fast, data-driven wind field predictor. You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to existing case studies. Where needed, you will contribute to field data collection to support model validation. You will share your results in stakeholder meetings, scientific conferences, and academic journals.

PhD in Trustworthy LLM-Enabled Ecosystems: From Requirements to Architecture

Large Language Models are increasingly becoming part of systems in which people, organizations, software systems and AI-enabled services work together. As these systems become more open and AI is given more responsibility, new questions arise. Who needs to trust whom, or what? What are they trusting them to do? Under what conditions are they willing to do so? And what does this mean for the requirements and architecture of these systems?

Many existing frameworks describe what trustworthy AI should provide, for example transparency, accountability, privacy, reliability, security and human oversight. The challenge is to translate these concerns into concrete requirements and architectural decisions. These concerns can also conflict. More transparency may affect privacy, for example, while more automation may reduce human control.

In this PhD, you will work at the intersection of requirements engineering, software architecture and trustworthy AI. You will study how different stakeholders understand trust in LLM-enabled systems and how their expectations, together with organizational and regulatory concerns, can be translated into requirements and architectural drivers. You will also study how different stakeholders can reason about choices concerning openness, information sharing, automation, human oversight and accountability.

You will develop and evaluate methods and models that help architects and other stakeholders identify trustworthiness requirements and make informed architectural decisions. Possible outcomes include approaches for eliciting and analyzing trust requirements, architecture principles or patterns, and contributions to a reference architecture. The exact direction and artefacts will develop during the PhD, giving you room to shape your own research questions and contributions.

This PhD is part of LLM4LM (Large Language Models for Logistic Management), a collaborative research project involving TU/e, TNO and several industry partners. The project investigates how LLMs and related AI technologies can support logistics and regulatory compliance in reliable, transparent and trustworthy ways. You will have the opportunity to study real AI adoption and architectural decision-making as it unfolds, working with academic and industry partners and across different application cases.

The research will use an empirical and design-oriented approach. Depending on the research questions, this can include interviews, workshops, document analysis, case studies, and the design and evaluation of methods or models. You will build on and contribute to ongoing research within LLM4LM while developing your own PhD research trajectory.

PhD-student: Physical Learning in Dynamical Systems

Work Activities
We are seeking a motivated PhD student to join our learning machines group at AMOLF and work on the theory of learning in dynamical physical systems, as part of an ERC Starting Grant project on Physical Learning in Dynamical Systems (PhyLDS).

Learning is often viewed as a computational process that takes place in brains or computers. Yet many physical systems, from biological networks to adaptive materials, continuously modify their behavior based on past experience. Despite the ubiquity of such adaptive phenomena, physics still lacks a general understanding of how learning emerges in dynamical systems that operate far from equilibrium.

In this project, we will develop a new theoretical framework for learning in physical systems with time-dependent dynamics. Unlike conventional machine learning algorithms, these systems learn through local interactions and physical feedback, without centralized optimization or backpropagation. We will investigate how learning is constrained by locality, causality, non-reciprocity, and dissipation, and how these constraints shape the ability of matter to learn.

The project combines analytical theory with large-scale numerical simulations. We will study diverse classes of adaptive dynamical networks, including flow networks, mechanical networks, and neuronal systems. A central goal is to identify the physical principles that govern learning in these systems, including scaling laws, phase diagrams, and fundamental limits.

The PhD student will contribute to:

• Developing local learning rules for dynamical physical systems;
• Comparing physical learning approaches to idealized gradient-based methods;
• Investigating when and why physical learning succeeds or fails;
• Exploring the role of feedback, non-equilibrium dynamics, and task complexity in learning;
• Developing efficient simulation tools for adaptive dynamical networks;
• Identifying scaling laws, phase boundaries, and universal features of learning in matter.

The project offers a unique opportunity to work at the intersection of condensed matter physics, non-equilibrium statistical mechanics, complex systems, machine learning, and biological physics. The successful candidate will help establish a new physics of adaptive matter and contribute to a growing international research effort aimed at understanding learning as a physical phenomenon.

For more information about our work, see:

[1] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond Matt Phys 14, 417 (2023)

[2] Stern, Hexner, Rocks and Liu, Supervised learning in physical networks: From machine learning to learning machines, Phys. Rev. X 11, 021045 (2021)

[3] Stern, Frim, Candás, Liu and Balasubramanian, Contrastive learning in tunable dynamical system, arXiv:2603.26969 (2026)

Qualifications
We seek candidates with a strong background in physics, mechanical engineering, materials science, or computer science with an interest in learning theory, broadly defined, condensed matter and complex 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 complex dynamical systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and welcome applicants with any background.

Work environment
AMOLF is a part of NWO-I and initiate and performs leading fundamental research on the physics of complex forms of matter, and to create new functional materials, in partnership with academia and industry. The institute is located at Amsterdam Science Park and currently employs about 140 researchers and 80 support employees. www.amolf.nl

The Learning Machines group is a new group at AMOLF, led by Menachem (Nachi) Stern, and focuses on the development of fundamental understanding and theories regarding learning, from a physical perspective, under real world constraints.

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 systems. We have a strong focus on stimulating development of students in all professional aspects, as well as collaborations with other researchers at our institute and beyond. Moreover, we work closely together with international groups and companies.

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, please contact:

Dr. Menachem Stern

E-mail: stern@amolf.nl

Application
You can respond to this vacancy online via the button below.

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).

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Werkgroepdocent Pedagogische Wetenschappen

Join us!
Wil jij studenten begeleiden in hun academische ontwikkeling? Wij zoeken per half december 2026 werkgroepdocenten voor het Bachelorprogramma Pedagogische wetenschappen binnen de Faculteit der Maatschappij- en Gedragswetenschappen.

Dit ga je doen
Binnen de driejarige Bacheloropleiding Pedagogische wetenschappen worden studenten opgeleid tot pedagogisch-academische professionals, die pedagogische vraagstukken op een academisch niveau kunnen analyseren en aanpakken, en hiermee een actieve bijdrage leveren aan een wetenschappelijk verantwoorde beroepspraktijk. Als belangrijk onderdeel van het opleidingsprogramma volgen studenten werkgroeponderwijs waarin zij academische-, onderzoeks- en klinische vaardigheden aanleren. Als werkgroepdocent begeleid je wekelijkse werkgroepen van ongeveer 16-18 studenten.

Meer specifiek zijn we op zoek naar docenten voor het volgende onderwijs:

• Werkgroepen waarin studenten klinische vaardigheden aanleren, zoals gespreksvaardigheden en observatievaardigheden.

De belangrijkste werkzaamheden van een werkgroepdocent bestaan uit:

• het (in samenwerking met collega’s) vormgeven van opdrachten en werkgroepen;
• het verzorgen van de werkgroepbijeenkomsten;
• het van feedback voorzien en beoordelen van opdrachten van studenten;
• het geven van persoonlijke begeleiding aan studenten;
• het nakijken van tentamens.

Dit vragen we van jou
Jouw ervaring en profiel:

• je beschikt over een afgeronde universitaire master Pedagogische Wetenschappen of Ontwikkelingspsychologie;
• je hebt aantoonbare ervaring of affiniteit met het geven van (academisch) onderwijs en het begeleiden van groepen studenten;
• je beschikt over goede wetenschappelijke schrijfvaardigheden;
• je hebt een werkhouding gericht op samenwerking, flexibiliteit en betrokkenheid;
• je bent georganiseerd en kunt goed plannen;
• je bent een enthousiaste en betrokken docent voor onze studenten;
• je beschikt over een registratie als basis/master-orthopedagoog en hebt bij voorkeur een basisaantekening diagnostiek;

Wat bieden we jou
We bieden een uitdagende functie met afwisselende werkzaamheden en volop ruimte voor eigen initiatief en ontwikkeling. We bieden je een dienstverband van 30.4 uur per week (0.8 fte), tenzij je zelf voorkeur hebt voor een geringere contractomvang (minimaal 19 uur per week, 0.5 fte), bij voorkeur met ingang van half december 2026. We bieden je een vierjarig contract als Docent 4 aan, met binnen het contract ruimte voor opleiding en ontwikkeling. Deze combinatie van het werken als docent en het volgen van scholing noemen wij het Docent Ontwikkel Programma (DOP). Gedurende je vierjarige contract volg je samen met andere docenten trainingen. Het DOP heeft als doel je arbeidsmarktpositie (inzetbaarheid en kansen) na het tijdelijke dienstverband te optimaliseren. Je komt in een enthousiast en warm team docenten dat openstaat voor nieuwe collega’s. Je gaat werken in een inspirerende academische en internationale werkomgeving in het hart van Amsterdam.

Je salaris bedraagt, afhankelijk van relevante ervaring bij aanvang van het dienstverband, €3.706 tot €5.760 bruto per maand (salarisschaal 10) op basis van volledige werktijd (38 uur per week). Daarnaast bieden we een uitgebreid pakket secundaire arbeidsvoorwaarden, waaronder een eindejaarsuitkering. Je hebt tevens recht op een ruime regeling aan vakantie uren, echter dien je deze tijdens de collegevrije periodes opnemen. Je kan geen vakantie opnemen tijdens les-/nakijkperiodes.

Hier ga je werken
De afdeling Pedagogische en Onderwijswetenschappen (POW) van de FMG is verantwoordelijk voor het onderwijs en onderzoek in de pedagogische en onderwijswetenschappen van de UvA. Medewerkers van de afdeling participeren in het College of Child Development and Education (bacheloronderwijs pedagogiek, onderwijswetenschappen en Universitaire Pabo van Amsterdam), de Graduate School of Child Development and Education ((pre-)masterprogramma’s pedagogiek, onderwijswetenschappen, lerarenopleidingen PO en VO en PhD-opleiding), het Research Institute of Child Development and Education (pedagogisch en onderwijskundig onderzoek) en Research Priority Area Yield (multidisciplinair onderzoek naar de menselijke ontwikkeling).