鲁汶大学PhD position in Advanced Brain PET Image Reconstruction申请条件要求-申请方
主办方
鲁汶大学
PhD直招介绍
The Molecular Image Reconstruction Group at KU Leuven (Department of Imaging & Pathology), led by Prof. Georg Schramm, conducts research at the intersection of medical image reconstruction, positron emission tomography (PET), and machine learning.
The group has close ties with UZ Leuven, one of Europe's leading academic hospitals, providing direct access to state-of-the-art clinical PET infrastructure and clinical expertise in nuclear medicine and neurology.
This position is embedded in the PEARL project (PET Enhancement via Advanced Reconstruction using variational methods and machine Learning), a tri-national Weave project funded by FWO (Belgium), FWF (Austria), and DFG (Germany), in collaboration with the University of Graz (Prof. Martin Holler) and TU Munich (Prof. Reinhard Heckel).
PEARL aims to advance static and dynamic brain PET image reconstruction through open benchmarking infrastructure, novel machine learning methods, and memory-efficient variational algorithms operating directly on raw PET data.
Website unit
( https://gschramm.github.io/ )
Responsibilities
As a PhD researcher you will contribute to the following tasks:
Data curation: retrospective collection, quality control, and preprocessing of approximately 500 static and dynamic brain PET/MR datasets acquired UZ Leuven during the last 10 years.
Open data infrastructure: pseudonymisation and conversion of raw listmode data into open, standardized formats.
GPU software development: design and optimization of high-performance, open-source time-of-flight (TOF) listmode forward and back-projectors for integration into ML frameworks such as PyTorch, building on the existing parallelproj library.
Method evaluation: critical assessment of reconstruction algorithms developed across all partner institutions, in close collaboration with clinical and kinetic modelling experts at UZ Leuven.
Challenge organisation: planning and execution of an open international brain PET reconstruction challenge, including data sharing agreements, automatic evaluation pipelines, and platform setup.
Dissemination: presentation of results at international conferences and publication in peer-reviewed journals.
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address( https://www.kuleuven.be/wieiswie/en/person/ue715673 ).
鲁汶大学 PhD position in Advanced Brain PET Image Reconstruction项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Offer
a fully funded 4-year PhD position within a stimulating, internationally connected research environment
direct access to state-of-the-art clinical PET infrastructure and clinical experts at UZ Leuven
high-performance GPU computing resources
participation in international conferences and project workshops (Leuven, Munich, Graz)
short-term research exchanges at partner institutions in Graz (Austria) and Munich (Germany)
a vibrant, international academic community in Leuven — a historic university city in the heart of Europe
鲁汶大学Phd申请条件和要求都有哪些？PhD position in Advanced Brain PET Image Reconstruction项目是不是全奖？有没有奖学金？下面我们一起看一下鲁汶大学申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Profile
Required:
Master's degree (or equivalent) in Engineering, Physics, Computer Science, Mathematics, or a closely related field, obtained before the start date
solid programming skills in Python (or other high-level languanges) and experience with scientific computing libraries (e.g. NumPy, PyTorch)
strong mathematical background in linear algebra, optimization, and probability/statistics
very good written and spoken English
enthusiasm for interdisciplinary research at the interface of medical imaging and machine learning
high intrinsic motivation
Advantageous (but optional):
prior experience with medical image reconstruction or emission tomography
experience in efficient handling of large research data sets
knowledge of variational methods, convex optimization, or iterative algorithms for inverse problems
experience with open-source software development and version control (Git)
exposure to deep learning methods
报名方式
招生人信息
Prof. Georg Schramm
邮箱：georg.schramm@kuleuven.be
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