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16.07.2025, Wissenschaftliches Personal A PhD position (75% TV-L E13, to be extended to 100% after 8 months) in discrete optimization is available at the Professorship of Optimization and
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Cancer Consortium (DKTK). For the DKTK partner site Munich, we are seeking for the next possible date a PhD Student in Mutational Processes Driving Somatic Evolution Reference number: 2025-0224 From
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 18 days ago
Biological sciences » Botany Biological sciences » Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 17 Sep 2025 - 23:59 (Europe/Berlin) Type
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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
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the emergence of FLT3-inhibitor resistant clones. The aim of this PhD position is to investigate primary cells, PDX and cell line models, and use a variety of methods to specifically identify adaptive changes in
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for improving the representation of biogeochemical processes in comprehensive Earth system models Detailed job description of all PhD projects Requirements We welcome applications from well motivated and
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: Reference number 3694: PhD student researching and developing alternative methods to animal testing (f/m/d) Pay grade: 13 TVöD Place of work: Berlin and Hannover Limited for three years Application deadline
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27.06.2025 Application deadline: 13.08.2025 The following position is available at the Department of Linguistics of the Faculty of Humanities beginning November 1st 2025 or shortly after. PhD
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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developing and using dedicated tools and processors Contribute to our sparse auto-differentiation libraries to accelerate the training of state-space models Collaborate closely with our internal partners