219 programming-"the"-"DAAD"-"IMPRS-ML"-"FEMTO-ST"-"U.S"-"Prof"-"https:" "DIFFER" Fellowship positions
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representation with deep-learnt visual features, toward machine uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a
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. The successful candidate will work part time as clinical research fellow supporting Professor Clark in his NIHR funded research programme on rapid diagnostic tests. The University of Southampton, Faculty
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will have specific responsibility for research and for developing research objectives and proposals on behalf of the Digital Cancer Screening Research Group. The role holder will be expected to plan and
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are guaranteed and reasonably priced relocation programme Diversity Diversity is a resource in the working and learning environment at UiS. When we have different backgrounds and experiences, we can approach
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capacity around Water and Sanitation in low- and middle-income settings across Southeast Asia. This position offers a unique opportunity to contribute to field-based public health research and program
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design: difference-in-differences and fixed effects models, instrumental variables, treatment selection, power analysis, analysis of attrition and non-compliance, analysis of heterogenous effects and
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programme aligned with the School’s strategic priorities, working alongside internationally recognised investigators in tumour biology, immuno-oncology, precision medicine, and cancer genomics. You will be
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program that advances the understanding within the scientific fields of brain and mental health. This position will contribute to the research priorities of the Turner Institute for Brain and Mental Health
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, probabilistic machine learning, or optimization methods is desirable Proficiency with relevant computational tools (Python, PyTorch/JAX, probabilistic programming) Personal skills: Ability to communicate science
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biology models Testing and evaluating new therapeutics in support of a drug discovery program Delivering high-quality, timely data to the MTDD–Servier joint project team Supporting project milestone