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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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scholar to join a multidisciplinary research program focused on understanding risk and resilience for psychiatric illness across development using multimodal human neuroscience approaches. The lab
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activities Requirements: PhD, preferably in Computer Engineering, Telecommunications, ICT Law, or related cybersecurity disciplines ANECA-accredited PhD and/or recognized research track record (e.g., six-year
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in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content The Konrad Zuse School of Excellence in reliable AI (relAI) offers fully funded
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in screening informatics, biological data analysis, and drug discovery workflows to support the development and delivery of screening and pharmacology programmes within the ARUK UCL Drug Discovery
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for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD in Immunology, Bioinformatics, Computational
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of experts? Then this job may be for you. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/296128/phd-fellowship-in-systems-neuroscience Where to apply Website https
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) Application Deadline 20 Apr 2026 - 23:59 (UTC) Country Belgium Type of Contract To be defined Job Status Full-time Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme
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Superior Técnico, Portugal and 27 associated partners (from 10 countries) Format: double PhD degree, granted by two universities in Europe; see the list of main partners: https://www.eu4greenfielddata.eu
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://eng.ed.ac.uk/studying/degrees/postgraduate-research/phd/integrated-unde… Requirements Research FieldEngineering » Electronic engineeringEducation LevelMaster Degree or equivalent Skills/Qualifications See: https