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PhD Position in Longitudinal Modeling of Digital Phenotyping for Quality of Life Trajectory Prediction in Cancer Survivors
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- Horas extra
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- Máster
- Machine learning
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PhD Position in Longitudinal Modeling of Digital Phenotyping for Quality of Life Trajectory Prediction in Cancer Survivors

Universidad de Deusto - Investigación
Bilbao, Vizcaya provincia

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Detalles del empleo

Hace 14 días

Cualificaciones

FP Grado Medio
Doctorado
Máster
Machine learning
Python

Descripción completa del empleo

The University of Deusto invites applications for this PhD position from motivated candidates interested in contributing to an international research project focused on digital phenotyping, artificial intelligence and quality of life in cancer survivors.

Purpose of the position:

This PhD position focuses on developing longitudinal models of digital phenotyping data to characterize and predict quality-of-life trajectories in cancer survivors. The project will use multimodal, repeatedly collected data from digital tools such as mobile apps, wearable sensors, and patient-reported outcome measures to capture changes in symptoms, functioning, behavior, and well-being over time. A central aim is to identify how quality of life evolves after cancer treatment and to determine which early digital signals are associated with recovery, stability, or decline.

The methodological focus will be on survival models and mixture models for trajectory modelling. Survival analysis will be used to study time-to-event outcomes such as clinically meaningful deterioration, recovery, relapse-related events, or transitions between health states, while mixture models will help identify latent subgroups of survivors with distinct quality-of-life patterns. The PhD candidate will contribute to statistical model development, validation, and interpretation, with the broader goal of supporting personalized survivorship care and timely interventions for individuals at risk of adverse quality-of-life outcomes.

Title of the project to be incorporated

Understanding and addressing LATE-effects of treatment of AYA cancer survivors with AI-based digital phenotyping and non-invasive holistic approach. Project Acronym (LATE_AYA)

PI and/or project manager

Aitor Almeida

We offer

Annual gross salary: €28,612.65

Duration of the contract: 24 months

International Mobility Support

In the case of an international appointment, the University of Deusto will provide institutional support for relocation and initial settlement, in accordance with its internal policies, in order to facilitate the candidate’s integration into the academic and professional environment.

Application and deadline

8th of june 2026

The University of Deusto carries out this call within the framework of “General call for Grants allocated to research projects or groups to pursue Doctoral Studies.”

Master’s degree (mathematical modeling, machine learning…)

Experience in modelling quality of life in cancer survivors.

Experience in Adaptive Hamiltonian Monte Carlo approaches.

Python

APPLICATION INSTRUCTIONS — PLEASE READ CAREFULLY

In order to apply to this job offer, all applicants must complete the following 2 steps:

1. Fill out the “Apply” section of this offer.

2. Complete the following APPLICATION FORM:

2.1 Enrollment at a University of Deusto PhD programme or the admissions letter for the 2026/2027 academic year. (see instructions for admission to a doctoral programme)

2.2 Official academic transcripts of previous official university studies (Undergraduate and Master/Postgraduate studies), even if they are currently being taken, issued by the corresponding unit. The academic certificate must state the name of the degree programme, the subjects that make up the course syllabus, the subjects passed, the grades obtained and the dates on which they were obtained. The transcript should show the average mark from 0 to 10.

2.3 Curriculum vitae.

Deadline: 08/06/2026 at 23:59 local time.

Contact information: [email protected]

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PhD Position in Longitudinal Modeling of Digital Phenotyping for Quality of Life Trajectory Prediction in Cancer Survivors

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