Postdoctoral Fellowship (04) at Vrije University Amsterdam, Netherlands

We are seeking a highly motivated junior researcher to support the activities within at least three projects (VU-based, national and international) and, if time allows, other projects within the Athena Institute at the VU Amsterdam. Your primary responsibility will be focused on three broader tasks – research, capacity building and project management. You will be involved in research for all three projects. With a project team, you will be involved in different phases of research (study design, data collection, data analysis, report/article writing, etc.). You will also be involved in capacity building initiatives within the projects – that include management and organization of living labs, trainings, workshops. Finally, you will be involved in project management – which includes tasks such as communication, coordination, network building, representation in different meetings, etc.

In this interesting postdoc position, through co-creative methods you will improve understanding and application of saline agriculture as a nature-based solution bringing biodiversity and human wellbeing together in saline landscapes. Using environmental policy analysis and economics, you will conduct research activities in our SaltyBEATS project (https://saltybeats.com), funded by Biodiversa+ under the call “Nature-Based Solutions for biodiversity, human well-being and transformative change (BiodivNBS)”. You will work at IVM Environmental Policy Analysis Department.

We are recruiting a highly motivated postdoctoral researcher to lead the first implementation of Deep-UV Raman spectroscopy for real-time polymer identification. This project addresses a critical gap: no established Deep-UV spectral libraries or real-time interpretation models for plastics currently exist. The successful candidate will therefore act as a first mover, building foundational datasets and AI models that enable material traceability and circularity in plastics.

The role focuses on developing and curating Deep-UV spectral databases, designing AI-based classification models, and further advancing the measurement instrument in close collaboration with our industrial partner, Veridis Technologies.

An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning frameworks and methods such as 1D-CNNs, transformers, or chemometrics for spectral analysis is highly valued, though applicants with strong backgrounds in physics, physical chemistry, or analytical chemistry are also encouraged to apply.

As a postdoctoral researcher, you will perform experimental work with a Deep-UV Raman spectroscopy setup, measuring representative plastic waste samples and optimising acquisition protocols to generate high-quality spectral data. You will build and curate a Deep-UV Raman spectral database and develop chemometric and AI models for real-time classification, additive detection, and inline interpretation. The work includes validating algorithms on a lab-scale sorting setup and linking spectroscopic outputs to recyclability scoring and Digital Product Passport data flows.

Acquired brain injury (ABI), including stroke, imposes a growing burden on Dutch healthcare. Every year thousands of patients face long-term limitations demanding rehabilitation and secondary prevention. Meanwhile, rehabilitation professionals face rising demand and staff shortages. Traditional rehabilitation programmes rely on in-clinic supervision, while current home-based training options are unstructured and poorly monitored. This leaves patients uncertain about exercise intensity and progress, therapists without timely information, and informal caregivers with limited support—undermining safety, efficiency, and outcomes.

TrAIn@home aims to design, integrate, and validate an AI-driven coaching platform for real-time, personalized, and safety-bounded home rehabilitation in adults with ABI. Multimodal data from wearables, context, and patient-reported outcomes are translated into actionable insights using AI-tools. Built-in safety rules and professional oversight ensure reliability and acceptance. Rather than replacing therapists, the platform strengthens and supports their role by providing structured information while giving patients guidance for safe, effective exercise at home.

By reducing unnecessary clinic visits, shortening waiting lists, and enabling more efficient allocation of therapist time, the TrAIn@home platform eases pressure on healthcare while lowering treatment costs. At the same time, it strengthens patient recovery through timely, personalized, and explainable feedback that enhances safety, motivation, and autonomy in home rehabilitation. Finally, TrAIn@home delivers validated multimodal datasets and integrates explainable, safety-bounded AI into clinical workflows. This advances understanding of dose–response relations and movement quality in ABI rehabilitation, while establishing transferable methods and infrastructures for other chronic conditions. TrAIn@home thus provides a foundation for sustained innovation in evidence-based, AI-assisted rehabilitation.