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Postdoctoral Fellow Position in Artificial Intelligence for Healthcare

Universität Bern -
Bern, BE

Jetzt bewerben

Details zur Stelle

Temporär | 100%
vor 9 Stunden

Qualifikationen

Informatik
Doktorat
Englisch
Master
Kommunikationsfähigkeit
Python

Vollständige Stellenbeschreibung

About us

The University of Bern is located at the heart of Switzerland. Internationally connected and regionally anchored, it cultivates exchange with society and strengthens partnerships between science, medicine, business, and politics. The University of Bern is committed to a deliberate and ethical responsibility towards people, animate and inanimate nature. As an important educator, promoting enterprise and industry in the region and beyond, it distinguishes itself through problem-oriented research into questions of pressing social relevance.

ARTORG is the University of Bern's transdisciplinary Center of Excellence for medical technology research. It tackles unmet clinical needs in diagnosis, monitoring, treatment and rehabilitation through innovative healthcare technology. In its unique constellation as an engineering department within a medical faculty, ARTORG has been delivering a decade of ambitious biomedical engineering research, teaching and development across a wide variety of clinical areas. Our master and PhD students work at the pulse of clinical reality to translate their skills and academic experiences directly into healthcare.

The position

The Artificial Intelligence in Health and Nutrition (AIHN) laboratory of the ARTORG Center is seeking a highly motivated and talented Postdoctoral Researcher to join our interdisciplinary team. We are looking for candidates with a strong background in Artificial Intelligence (AI), Machine Learning (ML), and Data Science, with particular expertise in at least two of the following areas: causal machine learning, reinforcement learning and generative AI, including multimodal foundation models.

We are looking for candidates with the following skills and experience:

PhD degree in Computer Science, AI/ML, Data Science, Biomedical Engineering, Electrical Engineering, Applied Mathematics, or a related field obtained within the last two years from a recognized university.
Strong publication record in leading AI/ML, CV, medical imaging or biomedical informatics journals and conferences.
Demonstrated expertise in modern AI methodologies.
Deep understanding of multimodal learning and foundation models, causal machine learning, reinforcement learning and / or Generative AI.
Excellent programming skills in Python and proficiency with modern deep learning frameworks.
Excellent oral and written communication skills in English.

The following qualifications would be considered an advantage:

Expertise with medical images.
Experience with large-scale datasets and distributed training.

The successful candidate will have the opportunity to work within a highly collaborative and international research environment and contribute to cutting-edge national and European research projects at the forefront of AI for health and biomedical discovery.

General Information about the Position

The position is funded for a period of 2 years at first, with the possibility of being prolonged for up to 4 years. Salary will be according to Swiss University regulations. The start date is expected to be in September 2026.

Applications will be reviewed until the position has been filled.

Application and Contact

Please send the application to
Prof. Dr. Stavroula Mougiakakou ([email protected]).
Applications should include: i) research statement, ii) detailed curriculum vitae, iii) list of publications, iv) contact details of three referees. The documents should be combined into ONE pdf file.

Questions about the position?

Please see 'Application and Contact'

Questions about the application?

Please see 'Application and Contact'

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Erweiterte Suche

python Jobs in bremgarten b. bern be

Postdoctoral Fellow Position in Artificial Intelligence for Healthcare

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PhD Position in Statistics with a focus on Statistical Machine Learning for Self-Driving Microscopy

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