Sort by
Refine Your Search
-
Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
-
, we are looking forward to your scientific support at the Clinic for Radiology! We are seeking a highly motivated PhD student to join our interdisciplinary research team working on multimodal imaging
-
histological validation within murine cancer models. We offer an association with the Collaborative Research Centre 1450 “Insight – Multiscale imaging of organ-specific inflammation” graduate school
-
), the TRR404 offers several PhD and Postdoc positions with starting dates from April 1, 2025 onwards. All vacancies can be found here: https://cfaed.tu-dresden.de/trr-vacancies . For TUD diversity is an
-
Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
-
mechanisms underlying psychosis. The research utilizes advanced multiphoton imaging and virtual reality platform for simultaneous recording of animal behaviour and microcircuit activity. RESPONSIBILITIES
-
for Studying Metabolic Plasticity (ECR 5) For further information please visit: https://www.umg.eu/en/potenziale-entfalten/ecr What we offer: Generous support (your own position, a postdoc (1.0), a PhD student
-
for Studying Metabolic Plasticity (ECR 5) For further information please visit: https://www.umg.eu/en/potenziale-entfalten/ecr What we offer: Generous support (your own position, a postdoc (1.0), a PhD student
-
fully equipped department using high resolution live cell imaging techniques and image data analysis as well as atomic force microscopy combined with biochemical and molecular technologies. We foster
-
architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning