AI Informed Adaptive Mid Field MRI For Personalized Treatment Of Depression

Laplace Apply; Amsterdam; Hybrid; EUR 42,000 - 65,000; Full time

Job summary

Amsterdam UMC offers a 12-month postdoc focused on AI-informed adaptive mid-field MRI to develop predictive biomarkers for antidepressant treatment. You will work with arterial spin labelling perfusion measurements and questionnaire data to refine models and workflows.

The role includes adapting prediction models, exploring reinforcement-learning strategies for optimal scanning, deploying a scanner prototype, and testing in a healthy-volunteer pilot within the PRESS platform.

Qualifications

• Education: PhD in artificial intelligence, computer science, engineering, physics, computational neuroscience, technical medicine or related area.

Responsibilities

• Develop predictive biomarkers for antidepressant treatment using adaptive mid-field MRI and arterial spin labelling perfusion measurements.
• Adapt clinical-trial prediction models and explore reinforcement-learning decisions about what to scan next.
• Deploy a prototype on the scanner and test it in a healthy-volunteer pilot within the PRESS platform.

Skills

Arterial spin labelling MRI

Reinforcement learning

Python

PyTorch

TensorFlow

Education

PhD in AI/CS/Engineering/Physics/Computational Neuroscience/Technical Medicine or related area

Job description

AI Informed Adaptive Mid Field MRI For Personalized Treatment Of Depression

Netherlands

10 November 2026

About This Opportunity

Amsterdam UMC offers AI Informed Adaptive Mid Field MRI For Personalized Treatment Of Depression. This 12-month Amsterdam UMC postdoc develops predictive biomarkers for antidepressant treatment using adaptive mid-field MRI, arterial spin labelling perfusion measurements an.

This 12-month Amsterdam UMC postdoc develops predictive biomarkers for antidepressant treatment using adaptive mid-field MRI, arterial spin labelling perfusion measurements and questionnaire-derived mood and symptom data. Work includes adapting clinical-trial prediction models, exploring reinforcement-learning decisions about what to scan next, deploying a prototype on the scanner and testing it in a healthy-volunteer pilot within the PRESS platform. Practical and application conditions: Contract or doctoral duration: 12 months; Advertised weekly workload: 36 hours/week; Published remuneration: EUR 3,724-5,867 gross per month; Stated application date: 10 November 2026 (cutoff time not independently validated).

Who This Opportunity Is Best Suited For: Based on the documented project scope, it may particularly suit applicants with a PhD in AI, computer science, biomedical engineering, physics, computational neuroscience, technical medicine or related subjects, strong Python/PyTorch/TensorFlow skills and interest in MRI, sequential decision-making or clinical prediction.

Potential Challenges / Who May Find It Less Suitable: The contract is limited to 12 months, and practical scanner integration and a human MRI pilot mean the work spans software development, imaging and clinical research.

Overall Opportunity Assessment: The distinguishing feature is the specific combination of research questions and methods described above; applicants should weigh this focus against the documented prerequisites and working conditions.

Level Researcher

12 months

Reference Code

365084

Funding & Compensation

Amount EUR 3,724-5,867 / Per Month

Funding Type Salary

Funding Status Paid

Period Per Month

Requirements

• Education: PhD in artificial intelligence, computer science, engineering, physics, computational neuroscience, technical medicine or related area.

Experience

• Technical Skills Arterial spin labelling MRI Reinforcement learning Python PyTorch TensorFlow