洛桑联邦理工大学、哥本哈根大学最新岗位制博士项目分享

本期为大家推荐洛桑联邦理工大学、哥本哈根大学最新2025岗位制博士项目信息。

洛桑联邦理工大学

PhD - Exploring the geometry of Markov diffusion processes to design more efficient AI algorithms

EPFL | LTS2 Lab

APPLICATION DEADLINE：招满即止

Mission

EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries.

The LTS2 Lab https://lts2.epfl.choffers a highly motivating, interdisciplinary scientific environment with many opportunities to interact between different projects and researchers, and has an excellent network of collaborative research projects with applications ranging from biology to neuroscience.

The objective of this project is to develop novel methodologies based on geometrical approaches, in particular Markov diffusion processes. These have the potential to heavily impact techniques such as diffusion or Transformers that are ubiquitous in modern GenAI approaches. There will be ample opportunities to do theory but also applications, for instance in GenAI for molecular biology.

We offer two PhD positions, fully funded by the Swiss National Science Foundation.

Profile

We are looking for PhD candidates with a strong analytical background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn geometric deep learning, signal processing, statistics and learning theory.

We expect the candidate to be self-driven with strong problem-solving abilities and out-of-the-box thinking.

Professional command of English (both written and spoken) is mandatory.

To be eligible for these positions you must also apply and be accepted to the doctoral program in Electrical Engineering. Please check this page for additional information. Please note that this is a separateapplication process.

Main duties and responsibilities

As a PhD Student, you will be expected to:

• Have full responsibility for your own dissertation
• Research in close collaboration with industry and academic partners;
• Experiment design and execution ;
• Analyze and interpret experimental results;
• Write scientific articles for publication in peer-reviewed journals;
• Present at international conferences;
• Supervise student projects.

We offer

• 4 years to complete your PhD with a competitive remuneration
• A world-class research and training environment with access to state-of-the-art research facilities;
• A multi-cultural and stimulating work environment;
• Term of employment: 1-year fixed-term contract (CDD), renewable for 4 years.

Informations

Only applications submitted through the online platform are considered.

• Your application should contain:
• Motivation Letter
• Detailed CV
• Contact information
• At least 2 references willing to write a recommendation letter.

For more information, please contact: pierre.vandergheynst@epfl.ch

Activity Rate : 100.00

Contract Type : PhD Student

Reference : 1317

哥本哈根大学

PhD fellowship in applied deep learning for image analysis at Department of Drug Design and Pharmacology

University of Copenhagen | Faculty of Health and Medical Sciences

APPLICATION DEADLINE: 3 Sep 2025,

We are offering a three-year PhD fellowship in image analysis using deep learning, commencing 1 December 2025 or as soon as possible thereafter.

Project description

We are seeking a highly motivated PhD student to work on a joint project between the Eye Translational Research Unit at The University of Copenhagen, the Section for Visual Computing at DTU Compute, and the Danish Glaucoma Association. The project, called "Project FOREVER: Application of deep learning models for better glaucoma detection in practice” is a subset of the ongoing FOREVER project (forever.ku.dk). The PhD project focuses on explainability of multimodal deep learning models for eye diseases based on, for example, fundus photographs and optical coherence tomography (OCT) scans of the participants in Project FOREVER.

In Project FOREVER, data from multiple modalities including OCT, fundus images, and electronic health records is collected from the FOREVER cohort consisting of costumers from a high street optician chain. Fundus photographs are widely used in Denmark, but OCT is a relatively new imaging method and not as widely incorporated in an optometric setting. Although deep learning models achieve high performances on public datasets for single modalities, they are not adapted in practice. During this PhD project, the research will be focused on finding ways to integrate deep learning models, in particular focusing on explainable deep learning methods, in a practical setting by:

• Investigating existing explainability methods for vision models used in ophthalmology
• Implementing existing methods and developing new methods for visual and multimodal explanations and user interactions
• Evaluation of different explainability methods in practice by interacting with different clinical user groups using, for example, questionnaires or interviews

As a PhD student on this project, you will have the opportunity to work with a team of experienced researchers, clinicians, and data scientists on tasks including data management and analysis, model development, and model evaluation. Your role will involve discussing and applying state-of-the-art deep learning methods to the FOREVER dataset. You will also be responsible for carrying out the analyses and assessing the results in a clinical context by interacting with different user profiles. As this is a joint project, it is expected that you will divide your time between the two different research environments at DTU Compute and University of Copenhagen. You will publish your findings in academic journals and present your work at both national and international meetings and conferences.

Principal supervisorProfessor Miriam Kolko, Department of Drug Design and Pharmacology

Start1 December 2025

Duration3 years as a PhD student

Our department

Department of Drug Design and Pharmacology is committed to research-based teaching and interdisciplinary research that supports the development and understanding of chemical and pharmacological properties of drugs and drug targets. We work as part of a dynamic academic community of research, education and innovation within the Pharmaceutical Sciences bridging Health Sciences and Life Sciences.

Information about the Department can be found at drug.ku.dk

Job descriptionYour key tasks as a PhD student at the Faculty of Health and Medical Sciences are:

• Carrying out an independent research project under supervision
• Completing PhD courses or other equivalent education corresponding to approximately 30 ECTS points
• Participating in active research environments including a stay at another research team, preferably abroad
• Obtaining experience with teaching or other types of dissemination related to your PhD project
• Teaching of undergraduate students at the department
• Writing a PhD thesis on the grounds of your project

Key criteria for the assessment of applicants

Applicants must have qualifications corresponding to a master’s degree related to the subject area of the project, e.g. computer science, biomedical engineering, data science, mathematics, or a related field. Please note that your master’s degree must be equivalent to a Danish master’s degree (two years).

The ideal candidate will also have the following skills:

• Programming experience in Python
• Hands-on experience with deep learning frameworks such as pytorch, and libraries such as scikit-learn, preferably for image analysis
• High level of motivation and innovation
• Experience with explainable AI is a plus
• Knowledge in the field of human- computer interaction is a plus
• Excellent writing- and communication skills in English
• Motivated to work in cross-disciplinary teams
• A team player who values sharing knowledge with colleagues and collaborators

Other important criteria are:

• Professional qualifications relevant to the PhD project
• Relevant work experience
• Previous publications
• The grade point average of the master’s degree
• A curious mindset with a strong interest in medical image analysis and the development of advanced analytical algorithms with clear clinical goals

Place of employment

The place of employment is at the at Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen. We offer creative and stimulating working conditions in dynamic and international research environment. Our research facilities include modern laboratories, state-of-the-art equipment and technology.

Terms of employment

The average weekly working hours are 37 hours per week.

The position is a fixed-term position limited to a period of 3 years.

Application procedure

Your application, in English, must be submitted electronically by clicking “Apply now” below and must include the following documents in PDF format:

1. Motivated letter of application (max. one page)
2. CV incl. education, experience, language skills and other skills relevant for the position
3. Certified copy of original Master of Science diploma and transcript of records in the original language, including an authorized English translation if issued in other language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted. As a prerequisite for a PhD fellowship employment, your master’s degree must be equivalent to a Danish master’s degree. We encourage you to read more in the assessment database: https://ufm.dk/en/education/recognition-and-transparency/find-assessments/assessment-database. Please note that we might ask you to obtain an assessment of your education performed by the Ministry of Higher Education and Science. Applicants with a Master´s degree from abroad should also enclose a short description of the grading scale used
4. Publication list (if possible)
5. Other information to consider: References and recommendations (if possible)

The deadline for applications is 3 September 2025, 23.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.