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scenarios and systematic literature review on algorithmic transparency and historical benchmarks in AI for healthcare. Selection and Evaluation of Methodologies: Identification and classification of XAI
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. Teaching & Curriculum Development Design and deliver advanced undergraduate and postgraduate courses in areas such as mathematical statistics, machine learning, coding, and algorithmics. Play a pivotal role
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» Algorithms Mathematics » Computational mathematics Mathematics » Mathematical logic Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 16 Feb 2026 - 22:59 (UTC) Type
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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Varaždin hereby invites A P P L I C A T I O N S for the appointment of one teacher to the teaching position of lecturer for scientific discipline of social sciences, field of information science
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mathematical statistics, machine learning, coding, and algorithmics. Contribute to programme design, accreditation, and innovative teaching strategies that enhance student experience and employability. Research
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the ortho/para (o/p) ratio of a liquid H2 solution will be performed: Test on a normal o/p H2 composition at Tamb Test on any o/p ratios between 0 and 75% These tests will be compared with reference results
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of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
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& league requirements regarding athlete onboarding each season (Transfers, LOIs, Nominations, Required training/education sessions etc.) Adhere to SAIT policies, procedures and protocols including p-card
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 1 month ago
. Test model predictions in behavioral experiments. Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems