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The **Computer Vision and Pattern Discovery (CVPD) group** at the **University of the Basque Country (UPV/EHU)** is offering a **fully funded 4-year PhD position** within the recently awarded project **TOSBI: Towards Scalable and Generalized Solutions in Biomedical Images**, supported by the Spanish State Agency for Research (AEI).
The official application for the 4-year PhD position is now <b>OPEN until October 31, 2025</b>!
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International candidates can apply — see below for full English instructions.
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The **Computer Vision and Pattern Discovery (CVPD) group** at the **University of the Basque Country (UPV/EHU)** is offering a **fully funded 4-year PhD position** within the recently awarded project **TOSBI: Towards Scalable and Generalized Solutions in Biomedical Images**, supported by the **Spanish State Agency for Research (AEI)**.
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<imgsrc="MICIU_Cofinanciado_AEI_logo.jpg"alt="Spanish State Agency for Research (AEI) Logo"width="500">
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### 🎯 About the PhD Project
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The doctoral research will focus on the **development of new deep learning methodologies for biomedical image analysis**, aiming to overcome key limitations such as **scarcity of annotated data, computational scalability, and model generalization**.
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The doctoral research will focus on the **development of new deep learning methodologies for biomedical image analysis**, addressing critical challenges such as **scarcity of annotated data, computational scalability, and model generalization**.
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The selected PhD student will investigate**partially supervised learning strategies** (self-/semi-supervised, few/zero-shot learning) and the **adaptation of foundation models** to bioimage analysis tasks, including **segmentation, classification, and object detection** of cellular and tissue structures. The work will combine **state-of-the-art AI methods** with validation on real biomedical data, contributing to the creation of **robust and accessible tools for the scientific community** through platforms such as [**BiaPy**](https://biapyx.github.io/).
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The selected PhD student will explore**partially supervised learning strategies** (self-/semi-supervised, few/zero-shot learning) and the **adaptation of foundation models** to bioimage analysis tasks, including **segmentation, classification, and object detection** of cellular and tissue structures. The project combines **state-of-the-art AI techniques** with **validation on real biomedical datasets**, contributing to the creation of **robust and accessible tools** for the research community through platforms such as [**BiaPy**](https://biapyx.github.io/).
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### 👩🎓 Candidate Profile
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- Degree and Master in **Computer Science, Mathematics, Telecommunication Engineering, Physics, Bioinformatics, or related fields**.
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- Degree and Master’s in **Computer Science, Mathematics, Telecommunication Engineering, Physics, Bioinformatics, or related fields**.
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- Skills in **scientific programming** (Python, PyTorch/TensorFlow, computer vision libraries).
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- Good command of **English** (spoken and written).
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-Strong motivation for **interdisciplinary research** at the interface of computer vision, deep learning, and biomedical imaging.
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-Motivation for **interdisciplinary research** at the interface of computer vision, deep learning, and biomedical imaging.
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Additional valued merits include prior experience in AI/bioimage projects, contributions to open-source software, and participation in international collaborations.
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Additional valued merits include prior experience in AI or bioimage projects, contributions to open-source software, and participation in international collaborations.
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### 📅 Timeline
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##📝 Official Application Procedure
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-**Call opening:** Expected October 10, 2025
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-**Deadline:** 3 weeks after call opening
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Applications must be submitted **through the official UPV/EHU FPI 2025 call**:
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