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                objective of this PhD position is to advance research in statistical signal processing and machine learning for microphone array data, with a focus on sensing under uncertainty. The successful candidate will 
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                transcriptomic data, you will identify protein signatures linked to tumour aggressiveness and metastasis and define multiomic biomarkers for improved cancer detection and patient stratification.\ You will join the 
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                . The file must include: A letter motivating the application (cover letter) Curriculum vitae (including name and contact information of a least two references) Grade transcripts and BSc/MSc diploma (in English 
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                The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the 
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                be one of the topics below. (PhD#1) Multi-Robot Systems (MRS). Study and development of MRS based on swarm robotics to navigate fields with robotic implements using Digital Twin information. The MRS 
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                this highly complex process. In addition, existing approaches are unable to produce diverse explanations, geared towards users with different information needs. ExplainYourself radically departs from existing 
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                porous carbon electrodes developed in the network. You will leverage advanced data analysis methods such as Distribution of Diffusion Times to obtain insight into mass transfer and microstructural effects 
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                , learning with limited training data, interpretable and explainable AI; as well as applications thereof to tasks such as fact checking and question answering. With a strong focus on both foundational and 
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                29 Oct 2025 Job Information Organisation/Company Technical University Of Denmark Department DTU Sustain Research Field Engineering » Materials engineering Engineering » Chemical engineering 
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                within the consortium. Provide data driven insights to guide the development of eco-friendly membrane technologies. Collaborating with fellow researchers across the UP2MEM network Participating in